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Application Data Value Characteristics Everything Is Not The Same (Part I)

Application Data Value Characteristics Everything Is Not The Same

Application Data Value Characteristics Everything Is Not The Same

Application Data Value Characteristics Everything Is Not The Same

This is part one of a five-part mini-series looking at Application Data Value Characteristics Everything Is Not The Same as a companion excerpt from chapter 2 of my new book Software Defined Data Infrastructure Essentials – Cloud, Converged and Virtual Fundamental Server Storage I/O Tradecraft (CRC Press 2017). available at Amazon.com and other global venues. In this post, we start things off by looking at general application server storage I/O characteristics that have an impact on data value as well as access.

Application Data Value Software Defined Data Infrastructure Essentials Book SDDC

Everything is not the same across different organizations including Information Technology (IT) data centers, data infrastructures along with the applications as well as data they support. For example, there is so-called big data that can be many small files, objects, blobs or data and bit streams representing telemetry, click stream analytics, logs among other information.

Keep in mind that applications impact how data is accessed, used, processed, moved and stored. What this means is that a focus on data value, access patterns, along with other related topics need to also consider application performance, availability, capacity, economic (PACE) attributes.

If everything is not the same, why is so much data along with many applications treated the same from a PACE perspective?

Data Infrastructure resources including servers, storage, networks might be cheap or inexpensive, however, there is a cost to managing them along with data.

Managing includes data protection (backup, restore, BC, DR, HA, security) along with other activities. Likewise, there is a cost to the software along with cloud services among others. By understanding how applications use and interact with data, smarter, more informed data management decisions can be made.

IT Applications and Data Infrastructure Layers
IT Applications and Data Infrastructure Layers

Keep in mind that everything is not the same across various organizations, data centers, data infrastructures, data and the applications that use them. Also keep in mind that programs (e.g. applications) = algorithms (code) + data structures (how data defined and organized, structured or unstructured).

There are traditional applications, along with those tied to Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), Big Data and other analytics including real-time click stream, media and entertainment, security and surveillance, log and telemetry processing among many others.

What this means is that there are many different application with various character attributes along with resource (server compute, I/O network and memory, storage requirements) along with service requirements.

Common Applications Characteristics

Different applications will have various attributes, in general, as well as how they are used, for example, database transaction activity vs. reporting or analytics, logs and journals vs. redo logs, indices, tables, indices, import/export, scratch and temp space. Performance, availability, capacity, and economics (PACE) describes the applications and data characters and needs shown in the following figure.

Application and data PACE attributes
Application PACE attributes (via Software Defined Data Infrastructure Essentials)

All applications have PACE attributes, however:

  • PACE attributes vary by application and usage
  • Some applications and their data are more active than others
  • PACE characteristics may vary within different parts of an application

Think of applications along with associated data PACE as its personality or how it behaves, what it does, how it does it, and when, along with value, benefit, or cost as well as quality-of-service (QoS) attributes.

Understanding applications in different environments, including data values and associated PACE attributes, is essential for making informed server, storage, I/O decisions and data infrastructure decisions. Data infrastructures decisions range from configuration to acquisitions or upgrades, when, where, why, and how to protect, and how to optimize performance including capacity planning, reporting, and troubleshooting, not to mention addressing budget concerns.

Primary PACE attributes for active and inactive applications and data are:

P – Performance and activity (how things get used)
A – Availability and durability (resiliency and data protection)
C – Capacity and space (what things use or occupy)
E – Economics and Energy (people, budgets, and other barriers)

Some applications need more performance (server computer, or storage and network I/O), while others need space capacity (storage, memory, network, or I/O connectivity). Likewise, some applications have different availability needs (data protection, durability, security, resiliency, backup, business continuity, disaster recovery) that determine the tools, technologies, and techniques to use.

Budgets are also nearly always a concern, which for some applications means enabling more performance per cost while others are focused on maximizing space capacity and protection level per cost. PACE attributes also define or influence policies for QoS (performance, availability, capacity), as well as thresholds, limits, quotas, retention, and disposition, among others.

Performance and Activity (How Resources Get Used)

Some applications or components that comprise a larger solution will have more performance demands than others. Likewise, the performance characteristics of applications along with their associated data will also vary. Performance applies to the server, storage, and I/O networking hardware along with associated software and applications.

For servers, performance is focused on how much CPU or processor time is used, along with memory and I/O operations. I/O operations to create, read, update, or delete (CRUD) data include activity rate (frequency or data velocity) of I/O operations (IOPS). Other considerations include the volume or amount of data being moved (bandwidth, throughput, transfer), response time or latency, along with queue depths.

Activity is the amount of work to do or being done in a given amount of time (seconds, minutes, hours, days, weeks), which can be transactions, rates, IOPs. Additional performance considerations include latency, bandwidth, throughput, response time, queues, reads or writes, gets or puts, updates, lists, directories, searches, pages views, files opened, videos viewed, or downloads.
 
Server, storage, and I/O network performance include:

  • Processor CPU usage time and queues (user and system overhead)
  • Memory usage effectiveness including page and swap
  • I/O activity including between servers and storage
  • Errors, retransmission, retries, and rebuilds

the following figure shows a generic performance example of data being accessed (mixed reads, writes, random, sequential, big, small, low and high-latency) on a local and a remote basis. The example shows how for a given time interval (see lower right), applications are accessing and working with data via different data streams in the larger image left center. Also shown are queues and I/O handling along with end-to-end (E2E) response time.

fundamental server storage I/O
Server I/O performance fundamentals (via Software Defined Data Infrastructure Essentials)

Click here to view a larger version of the above figure.

Also shown on the left in the above figure is an example of E2E response time from the application through the various data infrastructure layers, as well as, lower center, the response time from the server to the memory or storage devices.

Various queues are shown in the middle of the above figure which are indicators of how much work is occurring, if the processing is keeping up with the work or causing backlogs. Context is needed for queues, as they exist in the server, I/O networking devices, and software drivers, as well as in storage among other locations.

Some basic server, storage, I/O metrics that matter include:

  • Queue depth of I/Os waiting to be processed and concurrency
  • CPU and memory usage to process I/Os
  • I/O size, or how much data can be moved in a given operation
  • I/O activity rate or IOPs = amount of data moved/I/O size per unit of time
  • Bandwidth = data moved per unit of time = I/O size × I/O rate
  • Latency usually increases with larger I/O sizes, decreases with smaller requests
  • I/O rates usually increase with smaller I/O sizes and vice versa
  • Bandwidth increases with larger I/O sizes and vice versa
  • Sequential stream access data may have better performance than some random access data
  • Not all data is conducive to being sequential stream, or random
  • Lower response time is better, higher activity rates and bandwidth are better

Queues with high latency and small I/O size or small I/O rates could indicate a performance bottleneck. Queues with low latency and high I/O rates with good bandwidth or data being moved could be a good thing. An important note is to look at several metrics, not just IOPs or activity, or bandwidth, queues, or response time. Also, keep in mind that metrics that matter for your environment may be different from those for somebody else.

Something to keep in perspective is that there can be a large amount of data with low performance, or a small amount of data with high-performance, not to mention many other variations. The important concept is that as space capacity scales, that does not mean performance also improves or vice versa, after all, everything is not the same.

Where to learn more

Learn more about Application Data Value, application characteristics, PACE along with data protection, software defined data center (SDDC), software defined data infrastructures (SDDI) and related topics via the following links:

SDDC Data Infrastructure

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What this all means and wrap-up

Keep in mind that with Application Data Value Characteristics Everything Is Not The Same across various organizations, data centers, data infrastructures spanning legacy, cloud and other software defined data center (SDDC) environments. However all applications have some element (high or low) of performance, availability, capacity, economic (PACE) along with various similarities. Likewise data has different value at various times. Continue reading the next post (Part II Application Data Availability Everything Is Not The Same) in this five-part mini-series here.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Application Data Availability 4 3 2 1 Data Protection

Application Data Availability 4 3 2 1 Data Protection

4 3 2 1 data protection Application Data Availability Everything Is Not The Same

Application Data Availability 4 3 2 1 Data Protection

This is part two of a five-part mini-series looking at Application Data Value Characteristics everything is not the same as a companion excerpt from chapter 2 of my new book Software Defined Data Infrastructure Essentials – Cloud, Converged and Virtual Fundamental Server Storage I/O Tradecraft (CRC Press 2017). available at Amazon.com and other global venues. In this post, we continue looking at application performance, availability, capacity, economic (PACE) attributes that have an impact on data value as well as availability.

4 3 2 1 data protection Book SDDC

Availability (Accessibility, Durability, Consistency)

Just as there are many different aspects and focus areas for performance, there are also several facets to availability. Note that applications performance requires availability and availability relies on some level of performance.

Availability is a broad and encompassing area that includes data protection to protect, preserve, and serve (backup/restore, archive, BC, BR, DR, HA) data and applications. There are logical and physical aspects of availability including data protection as well as security including key management (manage your keys or authentication and certificates) and permissions, among other things.

Availability = accessibility (can you get to your application and data) + durability (is the data intact and consistent). This includes basic Reliability, Availability, Serviceability (RAS), as well as high availability, accessibility, and durability. “Durable” has multiple meanings, so context is important. Durable means how data infrastructure resources hold up to, survive, and tolerate wear and tear from use (i.e., endurance), for example, Flash SSD or mechanical devices such as Hard Disk Drives (HDDs). Another context for durable refers to data, meaning how many copies in various places.

Server, storage, and I/O network availability topics include:

  • Resiliency and self-healing to tolerate failure or disruption
  • Hardware, software, and services configured for resiliency
  • Accessibility to reach or be reached for handling work
  • Durability and consistency of data to be available for access
  • Protection of data, applications, and assets including security

Additional server I/O and data infrastructure along with storage topics include:

  • Backup/restore, replication, snapshots, sync, and copies
  • Basic Reliability, Availability, Serviceability, HA, fail over, BC, BR, and DR
  • Alternative paths, redundant components, and associated software
  • Applications that are fault-tolerant, resilient, and self-healing
  • Non disruptive upgrades, code (application or software) loads, and activation
  • Immediate data consistency and integrity vs. eventual consistency
  • Virus, malware, and other data corruption or loss prevention

From a data protection standpoint, the fundamental rule or guideline is 4 3 2 1, which means having at least four copies consisting of at least three versions (different points in time), at least two of which are on different systems or storage devices and at least one of those is off-site (on-line, off-line, cloud, or other). There are many variations of the 4 3 2 1 rule shown in the following figure along with approaches on how to manage technology to use. We will go into deeper this subject in later chapters. For now, remember the following.

large version application server storage I/O
4 3 2 1 data protection (via Software Defined Data Infrastructure Essentials)

4    At least four copies of data (or more), Enables durability in case a copy goes bad, deleted, corrupted, failed device, or site.
3    The number (or more) versions of the data to retain, Enables various recovery points in time to restore, resume, restart from.
2    Data located on two or more systems (devices or media/mediums), Enables protection against device, system, server, file system, or other fault/failure.

1    With at least one of those copies being off-premise and not live (isolated from active primary copy), Enables resiliency across sites, as well as space, time, distance gap for protection.

Capacity and Space (What Gets Consumed and Occupied)

In addition to being available and accessible in a timely manner (performance), data (and applications) occupy space. That space is memory in servers, as well as using available consumable processor CPU time along with I/O (performance) including over networks.

Data and applications also consume storage space where they are stored. In addition to basic data space, there is also space consumed for metadata as well as protection copies (and overhead), application settings, logs, and other items. Another aspect of capacity includes network IP ports and addresses, software licenses, server, storage, and network bandwidth or service time.

Server, storage, and I/O network capacity topics include:

  • Consumable time-expiring resources (processor time, I/O, network bandwidth)
  • Network IP and other addresses
  • Physical resources of servers, storage, and I/O networking devices
  • Software licenses based on consumption or number of users
  • Primary and protection copies of data and applications
  • Active and standby data infrastructure resources and sites
  • Data footprint reduction (DFR) tools and techniques for space optimization
  • Policies, quotas, thresholds, limits, and capacity QoS
  • Application and database optimization

DFR includes various techniques, technologies, and tools to reduce the impact or overhead of protecting, preserving, and serving more data for longer periods of time. There are many different approaches to implementing a DFR strategy, since there are various applications and data.

Common DFR techniques and technologies include archiving, backup modernization, copy data management (CDM), clean up, compress, and consolidate, data management, deletion and dedupe, storage tiering, RAID (including parity-based, erasure codes , local reconstruction codes [LRC] , and Reed-Solomon , Ceph Shingled Erasure Code (SHEC ), among others), along with protection configurations along with thin-provisioning, among others.

DFR can be implemented in various complementary locations from row-level compression in database or email to normalized databases, to file systems, operating systems, appliances, and storage systems using various techniques.

Also, keep in mind that not all data is the same; some is sparse, some is dense, some can be compressed or deduped while others cannot. Likewise, some data may not be compressible or dedupable. However, identical copies can be identified with links created to a common copy.

Economics (People, Budgets, Energy and other Constraints)

If one thing in life and technology that is constant is change, then the other constant is concern about economics or costs. There is a cost to enable and maintain a data infrastructure on premise or in the cloud, which exists to protect, preserve, and serve data and information applications.

However, there should also be a benefit to having the data infrastructure to house data and support applications that provide information to users of the services. A common economic focus is what something costs, either as up-front capital expenditure (CapEx) or as an operating expenditure (OpEx) expense, along with recurring fees.

In general, economic considerations include:

  • Budgets (CapEx and OpEx), both up front and in recurring fees
  • Whether you buy, lease, rent, subscribe, or use free and open sources
  • People time needed to integrate and support even free open-source software
  • Costs including hardware, software, services, power, cooling, facilities, tools
  • People time includes base salary, benefits, training and education

Where to learn more

Learn more about Application Data Value, application characteristics, PACE along with data protection, software defined data center (SDDC), software defined data infrastructures (SDDI) and related topics via the following links:

SDDC Data Infrastructure

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What this all means and wrap-up

Keep in mind that with Application Data Value Characteristics Everything Is Not The Same across various organizations, data centers, data infrastructures spanning legacy, cloud and other software defined data center (SDDC) environments. All applications have some element of performance, availability, capacity, economic (PACE) needs as well as resource demands. There is often a focus around data storage about storage efficiency and utilization which is where data footprint reduction (DFR) techniques, tools, trends and as well as technologies address capacity requirements. However with data storage there is also an expanding focus around storage effectiveness also known as productivity tied to performance, along with availability including 4 3 2 1 data protection. Continue reading the next post (Part III Application Data Characteristics Types Everything Is Not The Same) in this series here.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Application Data Characteristics Types Everything Is Not The Same

Application Data Characteristics Types Everything Is Not The Same

Application Data Characteristics Types Everything Is Not The Same

Application Data Characteristics Types Everything Is Not The Same

This is part three of a five-part mini-series looking at Application Data Value Characteristics everything is not the same as a companion excerpt from chapter 2 of my new book Software Defined Data Infrastructure Essentials – Cloud, Converged and Virtual Fundamental Server Storage I/O Tradecraft (CRC Press 2017). available at Amazon.com and other global venues. In this post, we continue looking at application and data characteristics with a focus on different types of data. There is more to data than simply being big data, fast data, big fast or unstructured, structured or semistructured, some of which has been touched on in this series, with more to follow. Note that there is also data in terms of the programs, applications, code, rules, policies as well as configuration settings, metadata along with other items stored.

Application Data Value Software Defined Data Infrastructure Essentials Book SDDC

Various Types of Data

Data types along with characteristics include big data, little data, fast data, and old as well as new data with a different value, life-cycle, volume and velocity. There are data in files and objects that are big representing images, figures, text, binary, structured or unstructured that are software defined by the applications that create, modify and use them.

There are many different types of data and applications to meet various business, organization, or functional needs. Keep in mind that applications are based on programs which consist of algorithms and data structures that define the data, how to use it, as well as how and when to store it. Those data structures define data that will get transformed into information by programs while also being stored in memory and on data stored in various formats.

Just as various applications have different algorithms, they also have different types of data. Even though everything is not the same in all environments, or even how the same applications get used across various organizations, there are some similarities. Even though there are different types of applications and data, there are also some similarities and general characteristics. Keep in mind that information is the result of programs (applications and their algorithms) that process data into something useful or of value.

Data typically has a basic life cycle of:

  • Creation and some activity, including being protected
  • Dormant, followed by either continued activity or going inactive
  • Disposition (delete or remove)

In general, data can be

  • Temporary, ephemeral or transient
  • Dynamic or changing (“hot data”)
  • Active static on-line, near-line, or off-line (“warm-data”)
  • In-active static on-line or off-line (“cold data”)

Data is organized

  • Structured
  • Semi-structured
  • Unstructured

General data characteristics include:

  • Value = From no value to unknown to some or high value
  • Volume = Amount of data, files, objects of a given size
  • Variety = Various types of data (small, big, fast, structured, unstructured)
  • Velocity = Data streams, flows, rates, load, process, access, active or static

The following figure shows how different data has various values over time. Data that has no value today or in the future can be deleted, while data with unknown value can be retained.

Different data with various values over time

Application Data Value across sddc
Data Value Known, Unknown and No Value

General characteristics include the value of the data which in turn determines its performance, availability, capacity, and economic considerations. Also, data can be ephemeral (temporary) or kept for longer periods of time on persistent, non-volatile storage (you do not lose the data when power is turned off). Examples of temporary scratch include work and scratch areas such as where data gets imported into, or exported out of, an application or database.

Data can also be little, big, or big and fast, terms which describe in part the size as well as volume along with the speed or velocity of being created, accessed, and processed. The importance of understanding characteristics of data and how their associated applications use them is to enable effective decision-making about performance, availability, capacity, and economics of data infrastructure resources.

Data Value

There is more to data storage than how much space capacity per cost.

All data has one of three basic values:

  • No value = ephemeral/temp/scratch = Why keep it?
  • Some value = current or emerging future value, which can be low or high = Keep
  • Unknown value = protect until value is unlocked, or no remaining value

In addition to the above basic three, data with some value can also be further subdivided into little value, some value, or high value. Of course, you can keep subdividing into as many more or different categories as needed, after all, everything is not always the same across environments.

Besides data having some value, that value can also change by increasing or decreasing in value over time or even going from unknown to a known value, known to unknown, or to no value. Data with no value can be discarded, if in doubt, make and keep a copy of that data somewhere safe until its value (or lack of value) is fully known and understood.

The importance of understanding the value of data is to enable effective decision-making on where and how to protect, preserve, and cost-effectively store the data. Note that cost-effective does not necessarily mean the cheapest or lowest-cost approach, rather it means the way that aligns with the value and importance of the data at a given point in time.

Where to learn more

Learn more about Application Data Value, application characteristics, PACE along with data protection, software-defined data center (SDDC), software-defined data infrastructures (SDDI) and related topics via the following links:

SDDC Data Infrastructure

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What this all means and wrap-up

Data has different value at various times, and that value is also evolving. Everything Is Not The Same across various organizations, data centers, data infrastructures spanning legacy, cloud and other software defined data center (SDDC) environments. Continue reading the next post (Part IV Application Data Volume Velocity Variety Everything Not The Same) in this series here.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Application Data Volume Velocity Variety Everything Is Not The Same

Application Data Volume Velocity Variety Everything Not The Same

Application Data Volume Velocity Variety Everything Not The Same

This is part four of a five-part mini-series looking at Application Data Value Characteristics everything is not the same as a companion excerpt from chapter 2 of my new book Software Defined Data Infrastructure Essentials – Cloud, Converged and Virtual Fundamental Server Storage I/O Tradecraft (CRC Press 2017). available at Amazon.com and other global venues. In this post, we continue looking at application and data characteristics with a focus on data volume velocity and variety, after all, everything is not the same, not to mention many different aspects of big data as well as little data.

Application Data Value Software Defined Data Infrastructure Essentials Book SDDC

Volume of Data

More data is growing at a faster rate every day, and that data is being retained for longer periods. Some data being retained has known value, while a growing amount of data has an unknown value. Data is generated or created from many sources, including mobile devices, social networks, web-connected systems or machines, and sensors including IoT and IoD. Besides where data is created from, there are also many consumers of data (applications) that range from legacy to mobile, cloud, IoT among others.

Unknown-value data may eventually have value in the future when somebody realizes that he can do something with it, or a technology tool or application becomes available to transform the data with unknown value into valuable information.

Some data gets retained in its native or raw form, while other data get processed by application program algorithms into summary data, or is curated and aggregated with other data to be transformed into new useful data. The figure below shows, from left to right and front to back, more data being created, and that data also getting larger over time. For example, on the left are two data items, objects, files, or blocks representing some information.

In the center of the following figure are more columns and rows of data, with each of those data items also becoming larger. Moving farther to the right, there are yet more data items stacked up higher, as well as across and farther back, with those items also being larger. The following figure can represent blocks of storage, files in a file system, rows, and columns in a database or key-value repository, or objects in a cloud or object storage system.

Application Data Value sddc
Increasing data velocity and volume, more data and data getting larger

In addition to more data being created, some of that data is relatively small in terms of the records or data structure entities being stored. However, there can be a large quantity of those smaller data items. In addition to the amount of data, as well as the size of the data, protection or overhead copies of data are also kept.

Another dimension is that data is also getting larger where the data structures describing a piece of data for an application have increased in size. For example, a still photograph was taken with a digital camera, cell phone, or another mobile handheld device, drone, or other IoT device, increases in size with each new generation of cameras as there are more megapixels.

Variety of Data

In addition to having value and volume, there are also different varieties of data, including ephemeral (temporary), persistent, primary, metadata, structured, semi-structured, unstructured, little, and big data. Keep in mind that programs, applications, tools, and utilities get stored as data, while they also use, create, access, and manage data.

There is also primary data and metadata, or data about data, as well as system data that is also sometimes referred to as metadata. Here is where context comes into play as part of tradecraft, as there can be metadata describing data being used by programs, as well as metadata about systems, applications, file systems, databases, and storage systems, among other things, including little and big data.

Context also matters regarding big data, as there are applications such as statistical analysis software and Hadoop, among others, for processing (analyzing) large amounts of data. The data being processed may not be big regarding the records or data entity items, but there may be a large volume. In addition to big data analytics, data, and applications, there is also data that is very big (as well as large volumes or collections of data sets).

For example, video and audio, among others, may also be referred to as big fast data, or large data. A challenge with larger data items is the complexity of moving over the distance promptly, as well as processing requiring new approaches, algorithms, data structures, and storage management techniques.

Likewise, the challenges with large volumes of smaller data are similar in that data needs to be moved, protected, preserved, and served cost-effectively for long periods of time. Both large and small data are stored (in memory or storage) in various types of data repositories.

In general, data in repositories is accessed locally, remotely, or via a cloud using:

  • Object and blobs stream, queue, and Application Programming Interface (API)
  • File-based using local or networked file systems
  • Block-based access of disk partitions, LUNs (logical unit numbers), or volumes

The following figure shows varieties of application data value including (left) photos or images, audio, videos, and various log, event, and telemetry data, as well as (right) sparse and dense data.

Application Data Value bits bytes blocks blobs bitstreams sddc
Varieties of data (bits, bytes, blocks, blobs, and bitstreams)

Velocity of Data

Data, in addition to having value (known, unknown, or none), volume (size and quantity), and variety (structured, unstructured, semi structured, primary, metadata, small, big), also has velocity. Velocity refers to how fast (or slowly) data is accessed, including being stored, retrieved, updated, scanned, or if it is active (updated, or fixed static) or dormant and inactive. In addition to data access and life cycle, velocity also refers to how data is used, such as random or sequential or some combination. Think of data velocity as how data, or streams of data, flow in various ways.

Velocity also describes how data is used and accessed, including:

  • Active (hot), static (warm and WORM), or dormant (cold)
  • Random or sequential, read or write-accessed
  • Real-time (online, synchronous) or time-delayed

Why this matters is that by understanding and knowing how applications use data, or how data is accessed via applications, you can make informed decisions. Also, having insight enables how to design, configure, and manage servers, storage, and I/O resources (hardware, software, services) to meet various needs. Understanding Application Data Value including the velocity of the data both for when it is created as well as when used is important for aligning the applicable performance techniques and technologies.

Where to learn more

Learn more about Application Data Value, application characteristics, performance, availability, capacity, economic (PACE) along with data protection, software-defined data center (SDDC), software-defined data infrastructures (SDDI) and related topics via the following links:

SDDC Data Infrastructure

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What this all means and wrap-up

Data has different value, size, as well as velocity as part of its characteristic including how used by various applications. Keep in mind that with Application Data Value Characteristics Everything Is Not The Same across various organizations, data centers, data infrastructures spanning legacy, cloud and other software defined data center (SDDC) environments. Continue reading the next post (Part V Application Data Access life cycle Patterns Everything Is Not The Same) in this series here.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Application Data Access Lifecycle Patterns Everything Is Not The Same

Application Data Access Life cycle Patterns Everything Is Not The Same(Part V)

Application Data Access Life cycle Patterns Everything Is Not The Same

Application Data Access Life cycle Patterns Everything Is Not The Same

This is part five of a five-part mini-series looking at Application Data Value Characteristics everything is not the same as a companion excerpt from chapter 2 of my new book Software Defined Data Infrastructure Essentials – Cloud, Converged and Virtual Fundamental Server Storage I/O Tradecraft (CRC Press 2017). available at Amazon.com and other global venues. In this post, we look at various application and data lifecycle patterns as well as wrap up this series.

Application Data Value Software Defined Data Infrastructure Essentials Book SDDC

Active (Hot), Static (Warm and WORM), or Dormant (Cold) Data and Lifecycles

When it comes to Application Data Value, a common question I hear is why not keep all data?

If the data has value, and you have a large enough budget, why not? On the other hand, most organizations have a budget and other constraints that determine how much and what data to retain.

Another common question I get asked (or told) it isn’t the objective to keep less data to cut costs?

If the data has no value, then get rid of it. On the other hand, if data has value or unknown value, then find ways to remove the cost of keeping more data for longer periods of time so its value can be realized.

In general, the data life cycle (called by some cradle to grave, birth or creation to disposition) is created, save and store, perhaps update and read with changing access patterns over time, along with value. During that time, the data (which includes applications and their settings) will be protected with copies or some other technique, and eventually disposed of.

Between the time when data is created and when it is disposed of, there are many variations of what gets done and needs to be done. Considering static data for a moment, some applications and their data, or data and their applications, create data which is for a short period, then goes dormant, then is active again briefly before going cold (see the left side of the following figure). This is a classic application, data, and information life-cycle model (ILM), and tiering or data movement and migration that still applies for some scenarios.

Application Data Value
Changing data access patterns for different applications

However, a newer scenario over the past several years that continues to increase is shown on the right side of the above figure. In this scenario, data is initially active for updates, then goes cold or WORM (Write Once/Read Many); however, it warms back up as a static reference, on the web, as big data, and for other uses where it is used to create new data and information.

Data, in addition to its other attributes already mentioned, can be active (hot), residing in a memory cache, buffers inside a server, or on a fast storage appliance or caching appliance. Hot data means that it is actively being used for reads or writes (this is what the term Heat map pertains to in the context of the server, storage data, and applications. The heat map shows where the hot or active data is along with its other characteristics.

Context is important here, as there are also IT facilities heat maps, which refer to physical facilities including what servers are consuming power and generating heat. Note that some current and emerging data center infrastructure management (DCIM) tools can correlate the physical facilities power, cooling, and heat to actual work being done from an applications perspective. This correlated or converged management view enables more granular analysis and effective decision-making on how to best utilize data infrastructure resources.

In addition to being hot or active, data can be warm (not as heavily accessed) or cold (rarely if ever accessed), as well as online, near-line, or off-line. As their names imply, warm data may occasionally be used, either updated and written, or static and just being read. Some data also gets protected as WORM data using hardware or software technologies. WORM (immutable) data, not to be confused with warm data, is fixed or immutable (cannot be changed).

When looking at data (or storage), it is important to see when the data was created as well as when it was modified. However, you should avoid the mistake of looking only at when it was created or modified: Instead, also look to see when it was the last read, as well as how often it is read. You might find that some data has not been updated for several years, but it is still accessed several times an hour or minute. Also, keep in mind that the metadata about the actual data may be being updated, even while the data itself is static.

Also, look at your applications characteristics as well as how data gets used, to see if it is conducive to caching or automated tiering based on activity, events, or time. For example, there is a large amount of data for an energy or oil exploration project that normally sits on slower lower-cost storage, but that now and then some analysis needs to run on.

Using data and storage management tools, given notice or based on activity, which large or big data could be promoted to faster storage, or applications migrated to be closer to the data to speed up processing. Another example is weekly, monthly, quarterly, or year-end processing of financial, accounting, payroll, inventory, or enterprise resource planning (ERP) schedules. Knowing how and when the applications use the data, which is also understanding the data, automated tools, and policies, can be used to tier or cache data to speed up processing and thereby boost productivity.

All applications have performance, availability, capacity, economic (PACE) attributes, however:

  • PACE attributes vary by Application Data Value and usage
  • Some applications and their data are more active than others
  • PACE characteristics may vary within different parts of an application
  • PACE application and data characteristics along with value change over time

Read more about Application Data Value, PACE and application characteristics in Software Defined Data Infrastructure Essentials (CRC Press 2017).

Where to learn more

Learn more about Application Data Value, application characteristics, PACE along with data protection, software defined data center (SDDC), software defined data infrastructures (SDDI) and related topics via the following links:

SDDC Data Infrastructure

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What this all means and wrap-up

Keep in mind that Application Data Value everything is not the same across various organizations, data centers, data infrastructures, data and the applications that use them.

Also keep in mind that there is more data being created, the size of those data items, files, objects, entities, records are also increasing, as well as the speed at which they get created and accessed. The challenge is not just that there is more data, or data is bigger, or accessed faster, it’s all of those along with changing value as well as diverse applications to keep in perspective. With new Global Data Protection Regulations (GDPR) going into effect May 25, 2018, now is a good time to assess and gain insight into what data you have, its value, retention as well as disposition policies.

Remember, there are different data types, value, life-cycle, volume and velocity that change over time, and with Application Data Value Everything Is Not The Same, so why treat and manage everything the same?

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Protection Diaries Fundamental Topics Tools Techniques Technologies Tips

Data Protection Fundamental Topics Tools Techniques Technologies Tips

Data Infrastructure and Data protection fundamental companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part I of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Protection Fundamental Infrastructure Essentials Book SDDC

The focus of this series is around data protection fundamental topics including Data Infrastructure Services: Availability, RAS, RAID and Erasure Codes (including LRC) ( Chapter 9), Data Infrastructure Services: Availability, Recovery Point ( Chapter 10). Additional Data Protection related chapters include Storage Mediums and Component Devices ( Chapter 7), Management, Access, Tenancy, and Performance ( Chapter 8), as well as Capacity, Data Footprint Reduction ( Chapter 11), Storage Systems and Solutions Products and Cloud ( Chapter 12), Data Infrastructure and Software-Defined Management ( Chapter 13) among others.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

Posts in this data protection fundamental series include:

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

Data Infrastructures

Data Infrastructures exists to support business, cloud and information technology (IT) among other applications that transform data into information or services. The fundamental role of data infrastructures is to provide a platform environment for applications and data that is resilient, flexible, scalable, agile, efficient as well as cost-effective.

Put another way, data infrastructures exist to protect, preserve, process, move, secure and serve data as well as their applications for information services delivery. Technologies that make up data infrastructures include hardware, software, or managed services, servers, storage, I/O and networking along with people, processes, policies along with various tools spanning legacy, software-defined virtual, containers and cloud. Read more about data infrastructures (its what’s inside data centers) here.

Why SDDC SDDI Need Data Protection
Various Needs Demand Drivers For Data Protection Fundamentals

Why The Need For Data Protection

Data Protection encompasses many different things, from accessibility, durability, resiliency, reliability, and serviceability ( RAS) to security and data protection along with consistency. Availability includes basic, high availability ( HA), business continuance ( BC), business resiliency ( BR), disaster recovery ( DR), archiving, backup, logical and physical security, fault tolerance, isolation and containment spanning systems, applications, data, metadata, settings, and configurations.

From a data infrastructure perspective, availability of data services spans from local to remote, physical to logical and software-defined, virtual, container, and cloud, as well as mobile devices. Figure 9.2 shows various data infrastructure availability, accessibility, protection, and security points of interest. On the left side of Figure 9.2 are various data protection and security threat risks and scenarios that can impact availability, or result in a data loss event ( DLE), data loss access ( DLA), or disaster. The right side of Figure 9.2 shows various techniques, tools, technologies, and best practices to protect data infrastructures, applications, and data from threat risks.

SDDI SDDC Data Protection Fundamental Big Picture
Figure 9.2 Various threat vectors, issues, problems, and challenges that drive the need for data protection

A fundamental role of data infrastructures (and data centers) is to protect, preserve, secure and serve information when needed with consistency. This also means that the data infrastructure resources (servers, storage, I/O networks, hardware, software, external services) and the applications (and data) they combine and are defined to protect are also accessible, durable and secure.

Data Protection topics include:

  • Maintaining availability, accessibility to information services, applications and data
  • Data include software, actual data, metadata, settings, certificates and telemetry
  • Ensuring data is durable, consistent, secure and recoverable to past points in time
  • Everything is not the same across different environments, applications and data
  • Aligning techniques and technologies to meet various service level objectives ( SLO)

Data Protection Fundamental Tradecraft Skills Experience Knowledge

Tools, technologies, trends are part of Data Protection, so to are the techniques of knowing (e.g. tradecraft) what to use when, where, why and how to protect against various threats risks (challenges, issues, problems).

Part of what is covered in this series of posts as well as in the Software Defined Data Infrastructure (SDDI) Essentials book is tradecraft skills, tips, experiences, insight into what to use, as well as how to use old and new things in new ways.

This means looking outside the technology box towards what is that you need to protect and why, then knowing how to use different skills, experiences, techniques part of your tradecraft combined with data protection toolbox tools. Read more about tradecraft here.

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

Everything is not the same across environments, data centers, data infrastructures and applications.

Likewise everything is and does not have to be the same when it comes to Data Protection. Data protection fundamentals encompasses many different hardware, software, services including cloud technologies, tools, techniques, best practices, policies and tradecraft experience skills (e.g. knowing what to use when, where, why and how).

Since everything is not the same, various data protection approaches are needed to address various application performance availability capacity economic ( PACE) needs, as well as SLO and SLAs.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 2 Reliability, Availability, Serviceability ( RAS) Data Protection Fundamentals.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Protection Diaries Reliability, Availability, Serviceability RAS Fundamentals

Reliability, Availability, Serviceability RAS Fundamentals

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 2 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 1 Data Infrastructure Data Protection Fundamentals, and click here to view the next post Part 3 Data Protection Access Availability RAID Erasure Codes (EC) including LRC.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Protection availability from Chapter 9 which includes access, durability, RAS, RAID and Erasure Codes (including LRC), mirroring and replication along with related topics.

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

Reliability, Availability, Serviceability (RAS) Data Protection Fundamentals

Reliability, Availability Serviceability (RAS) and other access availability along with Data Protection topics are covered in chapter 9. A resilient data infrastructure (software-defined, SDDC and legacy) protects, preserves, secures and serves information involving various layers of technology. These technologies enable various layers ( altitudes) of functionality, from devices up to and through the various applications themselves.

SDDI SDDC Data Protection Big Picture
Figure 9.2 Various threat issues and challenges that drive the need for data protection

Some applications need a faster rebuild, while others need sustained performance (bandwidth, latency, IOPs, or transactions) with the slower rebuild; some need lower cost at the expense of performance; others are ok with more space if other objectives are meet. The result is that since everything is different yet there are similarities, there is also the need to tune how data Infrastructure protects, preserves, secures, and serves applications and data.

General reliability, availability, serviceability, and data protection functionality includes:

  • Manually or automatically via policies, start, stop, pause, resume protection
  • Adjust priorities of protection tasks, including speed, for faster or slower protection
  • Fast-reacting to changes, disruptions or failures, or slower cautious approaches
  • Workload and application load balancing (performance, availability, and capacity)

RAS can be optimized for:

  • Reduced redundancy for lower overall costs vs. resiliency
  • Basic or standard availability (leverage component plus)
  • High availability (use better components, multiple systems, multiple sites)
  • Fault-tolerant with no single points of failure (SPOF)
  • Faster restart, restore, rebuild, or repair with higher overhead costs
  • Lower overhead costs (space and performance) with lower resiliency
  • Lower impact to applications during rebuild vs. faster repair
  • Maintenance and planned outages or for continues operations

Common availability Data Protection related terms, technologies, techniques, trends and topics pertaining to data protection from availability and access to durability and consistency to point in time protection and security are shown below.

Data Protection Gaps and Air Gap

There are Good Data Protection Gaps that provide recovery points to a past time enabling recoverability in the future to move forward. Another good data protection gap is an Air Gap that isolates protection copies off-site or off-line so that they can not be tampered with enabling recovery from ransomware and other software defined threats. There are Bad data protection gaps including gaps in coverage where data is not protected or items are missing. Then there are Ugly data protecting gaps which include Bad gaps that result in what you think is protected are not and finding that your copies are bad when it is too late.

Data Protection Gaps Good Bad Ugly
Data Protection Gaps Good Bad and Ugly

The following figure shows good data protection gaps including recovery points (point in time protection) along with air gaps.

Good Data Protection Gaps
Figure 9.9 Air Gaps and Data Protection

Fault / Failures To Tolerate (FTT)

FTT is how many faults or failures to tolerate for a given solution or service which in turn determines what mode of protection, or fault tolerant mode ( FTM) to use.

Fault Tolerant Mode (FTM)

FTM is the mode or technique used to enable resiliency and protect against some number of faults.

Fault / Failure Domains

Fault or Failure domains are places and things that can fail from regions, data centers or availability zones, clusters, stamps, pods, servers, networks, storage, hardware (systems, components including SSD and HDDs, power supplies, adapters). Other fault domain topics and focus areas include facility power, cooling, software including applications, databases, operating systems and hypervisors among others.

SDDI SDDC Fault Domains Zones Regions
Figure 9.5 Various Fault and Failure Domains, Regions, Locations

Clustering

Clustering is a technique and technology for enabling resiliency, as well as scaling performance, availability, and capacity. Clusters can be local, remote, or wide-area to support different data infrastructure objectives, combined with replication and other techniques.

SDDI SDDC Clustering
Figure 9.12 Clustering and Replication Examples

Another characteristic of clustering and resiliency techniques is the ability to detect and react quickly to failures to isolate and contain faults, as well as invoking automatic repair if needed. Different clustering technologies enable various approaches, from proprietary hardware and software tightly coupled to loosely coupled general-purpose hardware or software.

Clustering characteristics include:

  • Application, database, file system, operating system (Windows Storage Replica)
  • Storage systems, appliances, adapters and network devices
  • Hypervisors ( Hyper-V, VMware vSphere ESXi and vSAN among others)
  • Share everything, share some things, share nothing
  • Tightly or loosely coupled with common or individual system metadata
  • Local in a data center, campus, metro, or stretch cluster
  • Wide-area in different regions and availability zones
  • Active/active for fast fail over or restart, active/passive (standby) mode

Additional clustering considerations include:

  • How does performance scale as nodes are added, or what overhead exists?
  • How is cluster resource locking in shared environments handled?
  • How many (or few) nodes are needed for quorum to exist?
  • Network and I/O interface (and management) requirements
  • Cluster partition or split-brain (i.e., cluster splits into two)?
  • Fast-reacting fail over and resiliency vs. overhead of failing back
  • Locality of where applications are located vs. storage access and clustering

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

Everything is not the same across different environments, data centers, data infrastructures and applications. There are various performance, availability, capacity economic (PACE) considerations along with service level objectives (SLO). Availability means being able to access information resources (applications, data and underlying data infrastructure resources), as well as data being consistent along with durable. Being durable means enabling data to be accessible in the event of a device, component or other fault domain item failures (hardware, software, data center).

Just as everything is not the same across different environments, there are various techniques, technologies and tools that can be used in different ways to enable availability and accessibility. These include high availability (HA), RAS, mirroring, replication, parity along with derivative erasure code (EC), LRC, RS and other RAID implementations, along with clustering. Also keep in mind that pertaining to data protection, there are good gaps (e.g. time intervals for recovery points, air gaps), bad gaps (missed coverage or lack of protection), and ugly gaps (not being able to recover from a gap in time).

Note that mirroring, replication, EC, LRC, RS or other Parity and RAID approaches are not replacements for backup, rather they are companions to time interval based recovery point protection such as snapshots, backup, checkpoints, consistency points and versioning among others (discussed in follow-up posts in this series).

Which data protection tool, technology to trend is the best depends on what you are trying to accomplish and your application workload PACE requirements along with SLOs. Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 3 Data Protection Access Availability RAID Erasure Codes (EC) including LRC.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

Posted on

Data Protection Diaries Access Availability RAID Erasure Codes LRC Deep Dive

Access Availability RAID Erasure Codes including LRC Deep Dive

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 3 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 2 Reliability, Availability, Serviceability (RAS) Data Protection Fundamentals, and click here to view the next post Part 4 Data Protection Recovery Points (Archive, Backup, Snapshots, Versions).

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post part of the Data Protection diaries series as well as companion to Chapter 9 of SDDI Essentials book, we are going on a longer, deeper dive. We are going to look at availability, access and durability including mirror, replication, RAID including various traditional and newer parity approaches such as Erasure Codes ( EC), Local Reconstruction Code (LRC), Reed Solomon (RS) also known as RAID 2 among others. Later posts in this series look at point in time data protection to support recovery to a given time (e.g. RPO), while this and the previous post look at maintaining access and availability.

Keep in mind that if something can fail, it probably will, also that everything is not the same meaning different environments, application workloads (along with their data). Different environments and applications have diverse performance, availability, capacity economic (PACE) attributes, along with service level objectives ( SLOs). Various SLOs include PACE attributes, recovery point objectives ( RPO), recovery time objective ( RTO) among others.

Availability, accessibility and durability (see part two in this series) along with associated RAS topics are part of what enable RTO, as well as meet Faults (or failures) to tolerate ( FTT). This means that different fault tolerance modes ( FTM) determine what technologies, tools, trends and techniques to use to meet different RTO, FTT and application PACE needs.

Maintaining access and availability along with durability (e.g. how many copies of data as well as where stored) protects against loss or failure of a component device ( SSD, HDDs, adapters, power supply, controller), node or system, appliance, server, rack, clusters, stamps, data center, availability zones, regions, or other Fault or Failure domains spanning hardware, software, and services.

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

Data Protection Access Availability RAID Erasure Codes

This is a good place to mention some context for RAID and RAID array, which can mean different things pertaining to Data Protection. Some people associate RAID with a hardware storage array, or with a RAID card. Other people consider an array to be a storage array that is a RAID enabled storage system. A trend is to refer to legacy storage systems as RAID arrays or hardware-based RAID, to differentiate from newer implementations.

Context comes into play in that a RAID group (i.e., a collection of HDDs or SSD that is part of a RAID set) can be referred to as an array, a RAID array, or a virtual array. What this means is that while some RAID implementations may not be relevant, there are many new and evolving variations extending parity based protection making at least software-defined RAID still relevant

Keep context in mind, and don’t be afraid to ask what someone is referring to: a particular vendor storage system, a RAID implementation or packaging, a storage array, or a virtual array. Also keep the context of the virtual array in perspective vs. storage virtualization and virtual storage. RAID as a term is used to refer to different modes such as mirroring or parity, and parity can be legacy RAID 4, 5, or 6 along with erasure codes (EC). Note some people refer to erasure codes in the context of not being a RAID system, which can be an inference to not being a legacy storage system running hardware RAID (e.g. not software or software defined).

The following figure (9.13) shows various availability protection schemes (e.g. not recovery point) that maintain access while protecting against loss of a component, device, system, server, site, region or other part of a fault domain. Since everything is not the same with environments and applications having different Performance Availability Capacity Economic ( PACE) attributes, there are various approaches for enabling availability along with accessibility.

Keep in mind that RAID and Erasure codes along with their various, as well as replication and mirroring by themselves are not a replacement for backup or other point in time (e.g. enable recovery point) protection.

Instead, availability technologies such as RAID and erasure code along with mirror as well as replication need to be combined with snapshots, point in time copies, consistency points, checkpoints, backups among other recovery point protection for complete data protection.

Speaking of replacement for backup, while many vendors and their pundits claIm or want to see backup as being dead, as long as they keep talking about backup instead of broader data protection backup will remain alive.

SDDC SDDI RAID Parity Erasure Code EC
Figure 9.13 Various RAID, Mirror, Parity and Erasure Code (EC) approaches

Different RAID levels (including parity, EC, LRC and RS based) will affect storage energy effectiveness, similar to various SSD or HDD performance capacity characteristics; however, a balance of performance, availability, capacity, and energy needs to occur to meet application service needs. For example, RAID 1 mirroring or RAID 10 mirroring and striping use more HDDs and, thus, power, but will yield better performance than RAID 6 and erasure code parity protection.

 

Normal performance

 

Availability

Performance overhead

Rebuild overhead

Availability overhead

RAID 0 (stripe)

Very good read & write

None

None

Full volume restore

None

RAID 1 (mirror or replicate)

Good reads; writes = device speed

Very good; two or more copies

Multiple copies can benefit reads

Re-synchronize with existing volume

2:1 for dual, 3:1 for three-way copies

RAID 4 (stripe with dedicated parity, i.e., 4 + 1 = 5 drives total)

Poor writes without cache

Good for smaller drive groups and devices

High on write without cache (i.e., parity)

Moderate to high, based on number and type of drives

Varies; 1 Parity/N, where N = number of devices

RAID 5
(stripe with rotating parity, 4 + 1 = 5 drives)

Poor writes without cache

Good for smaller drive groups and devices

High on write without cache (i.e., parity)

Moderate to high, based on number and type of drives

Varies
1 Parity/N, where N = number of devices

RAID 6
(stripe with dual parity, 4 + 2 = 6 drives)

Poor writes without cache

Better for larger drive groups and devices

High on write without cache (i.e., parity)

Moderate to high, based on number and type of drives

Varies; 2 Parity/N, where N = number of devices

RAID 10
(mirror and stripe)

Good

Good

Minimum

Re-synchronize with existing volume

Twice mirror capacity stripe drives

Reed-Solomon (RS) parity, also known as erasure code (EC), local reconstruction code (LRC), and SHEC

Ok for reads, slow writes; good for static and cold data with front-end cache

Good

High on writes (CPU for parity calculation, extra I/O operations)

Moderate to high, based on number and type of drives, how implemented, extra I/Os for reconstruction

Varies, low overhead when using large number of devices; CPU, I/O, and network overhead.

Table 9.3 Common RAID Characteristics

Besides those shown in table 9.3, other RAID including parity based approaches include 2 (Reed Solomon), 3 (synchronized stripe and dedicated parity) along with others including combinations such as 10, 01, 50, 60 among others.

Similar to legacy parity-based RAID, some erasure code implementations use narrow drive groups while others use larger ones to increase protection and reduce capacity overhead. For example, some larger enterprise-class storage systems (RAID arrays) use narrow 3 + 1 or 4 + 1 RAID 5 or 4 + 2 or 6 + 2 RAID 6, which have higher protection storage capacity overhead and fault=impact footprint.

On the other hand, many smaller mid-range and scale-out storage systems, appliances, and solutions support wide stripes such as 7 + 1, 15 + 1, or larger RAID 5, or 14 + 2 or larger RAID 6. These solutions trade the lower storage capacity protection overhead for risk of a multiple drive failures or impacts. Similarly, some EC implementations use relatively small groups such as 6, 2 (8 drives) or 4, 2 (6 drives), while others use 14, 4 (18 drives), 16, 4 (20 drives), or larger.

Table 9.4 shows options for a number of data devices (k) vs. a number of protect devices (m).

k
(data devices)

m
(protect devices)

Availability;
Resiliency

Space capacity overhead

Normal performance

FTT

Comments;
Examples

Narrow

Wide

Very good;
Low impact of rebuild

Very high

Good (R/W)

Very good

Trade space for RAS;
Larger m vs. k;
1, 1; 1, 2; 2, 2; 4, 5

Narrow

Narrow

Good

Good

Good (R/W)

Good

Use with smaller drive groups;
2, 1; 3, 1; 6, 2

Wide

Narrow

Ok to good;
With larger m value

Low as m gets larger

Good (read);
Writes can be slow

Ok to good

Smaller m can impact rebuild;
3, 1; 7, 1; 14, 2; 13, 3

Wide

Wide

Very good;
Balanced

High

Good

Very good

Trade space for RAS;
2, 2; 4, 4; 8, 4; 18, 6

Table 9.4. Comparing Various Data Device vs. Protect Device Configurations

Note that wide k with no m, such as 4, 0, would not have protection. If you are focused on reducing costs and storage space capacity overhead, then a wider (i.e., more devices) with fewer protect devices might make sense. On the other hand, if performance, availability, and minimal to no impact during rebuild or reconstruction are important, then a narrower drive set, or a smaller ratio of data to protect drives, might make sense.

Also note that the higher or larger the RAID number, or parity scheme, or number of "m" devices in a parity and erasure code group may not be better, likewise smaller may not be better. What is better is which approach meets your specific application performance, availability, capacity, economic (PACE) needs, along with SLO, RTO, RPO requirements. What can also be good is to use hybrid approaches combining different technologies and tools to facilitate both access, availability, durability along with point in time recovery across different layers of granularity (e.g. device, drive, adapter, controller, cabinet, file system, data center, etc).

Some focus on the lower level RAID as the single or primary point of protection, however watch out for that being your single point of failure as well. For example, instead of building a resilient RAID 10 and then neglecting to have adequate higher level access, as well as recovery point protection, combine different techniques including file system protection, snapshots, and backups among others.

Figure 9.14 shows various options and considerations for balancing between too many or too few data (k) and protect (m) devices. The balance is about enabling particular FTT along with PACE attributes and SLO. This means, for some environments or applications, using different failure-tolerant modes ( FTM) in various combinations as well as configurations.

SDDC SDDI Data Protection
Figure 9.14 Comparing various data drive to protection devices

Figure 9.14 top shows no protection overhead (with no protection); the bottom shows 13 data drives and three protection drives in an EC (RS or LRC among others) configuration that could tolerate three devices failing before loss of data or access occurs. In between are various options that can also be scaled up or down across a different number of devices ( HDDs, SSD, or systems).

Some solutions allow the user or administrator to configure the I/O chunk, slabs, shard, or stripe size, for example, from 8 KB to 256 KB to 1 MB (or larger), aligning with application workload and I/O profiles. Other options include the ability to set or disable read-ahead, write-through vs. write-back cache (with battery-protected cache), among other options.

The width or number of devices in a RAID parity or erasure group is based on a combination of factor, including how much data is to be stored and what your FTT objective is, along with spreading out protection overhead. Another consideration is whether you have large or small files and objects.

For example, if you have many small files and a wide stripe, parity, or erasure code set with a large chunk or shard size, you may not have an optimal configuration from a performance perspective.

The following figure shows combing various data protection availability and accessibility technologies including local as well as remote mirroring and replication, along with parity or erasure code (including LRC, RS, SHEC among others) approaches. Instead of just using one technology, a hybrid approach is used leveraging mirror (local on SSD) and replication across sites including asynchronous and synchronous. Replication modes include Asynchronous (time-delayed, eventual consistency) for longer distance, higher latency networks, and synchronous (strong consistency, real-time) for short distance or low-latency networks.

Note that the mirror and replication can be done in software deployed as part of a storage system, appliance or as tin-wrapped software, virtual machine, virtual storage appliance, container or some other deployment mode. Likewise RAID, parity and erasure code software can be deployed and packaged in different ways.

In addition to mirror and replication, solutions are also using parity based including erasure code variations for lower cost, less active data. In other words, the mirror on SSD handles active hot data, as well as any buffering or cache, while lower performance, higher capacity, lower cost data gets de-staged or migrated to a parity erasure code tier. Some vendors, service provider and solutions leveraging variations of the approach in figure 9.15 include Microsoft ( Azure and Windows) and VMware among others.

SDDC SDDI Data Protection
Figure 9.15 Combining various availability data protection techniques

A tradecraft skill is finding the balance, knowing your applications, the data, and how the data is allocated as well as used, then leveraging that insight and your experience to configure to meet your application PACE requirements.

Consider:

  • Number of drives (width) in a group, along with protection copies or parity
  • Balance rebuild performance impact and time vs. storage space overhead savings
  • Ability to mix and match various devices in different drive groups in a system
  • Management interface, tools, wizards, GUIs, CLIs, APIs, and plug-ins
  • Different approaches for various applications and environments
  • Context of a physical RAID array, system, appliance, or solution vs. logical

Erasure Codes (EC)

Erasure Codes ( EC) combines advanced protection with variable space capacity overhead over many drives, devices, or systems using large parity chunks, shards compared to traditional parity RAID approaches. There are many variations of EC as well as parity based approaches, some are tied to Reed Solomon (RS) codes while others use different approaches.

Note that some EC are optimized for reducing the overhead and cost of storing data (e.g. less space capacity) for inactive, or primarily read data. Likewise, some EC or variations are optimized for performance of reads/writes as well as reducing overhead of rebuild, reconstructions, repairs with least impact. Which EC or parity derivative approach is best depends on what you are trying to do or impact to avoid.

Reed Solomon (RS) codes

Reed Solomon (RS) codes are advanced parity protection mathematical algorithm technique that works well on large amounts of data providing protection with lower space capacity overhead depending on how configured. Many Erasure Codes (EC) are based on derivatives of RS. Btw, did you know (or remember) that RAID 2 (rarely used with few legacy implementations) has ties to RS codes? Here are some additional links to RS including via Backblaze, CMU, and Dr Dobbs.

Local Reconstruction Codes (LRC)

Microsoft leverages LRC in Azure as well as in Windows Servers. LRC are optimized for a balance of protection, space capacity savings, normal performance as well as reducing impact on running workloads during a repair, rebuild or reconstruction. One of the tradeoffs that LRC uses is to add some amount of additional space capacity in exchange for normal and abnormal (e.g. during repair) performance improvements. Where RS, EC and other parity based derivatives typically use a (k,m) nomenclature (e.g. data, protection), LRC adds an extra variable to help with constructions (k,m,n).

Some might argue that LRC are not as space efficient as other EC, RS or parity derivative variations of which the counter argument can be that some of those approaches are not as performance effective. In other words, everything is not the same, one approach does not or should not have to be applied to all, unless of course your preferred solution approach can only do one thing.

Additional LRC related material includes:

  • (PDF by Microsoft) LRC Erasure Coding in Windows Storage Spaces
  • (Microsoft Usenix Paper) Best Paper Award Erasure Coding in Azure
  • (Via MSDN Shared) Azure Storage Erasure Coding with LRC
  • (Via Microsoft) Azure Storage with Strong Consistency
  • (Paper via Microsoft) 23rd ACM Symposium on Operating Systems Principles (SOSP)
  • (Microsoft) Erasure Coding in Azure with LRC
  • (Via Microsoft) Good collection of EC, RS, LRC and related material
  • (Via Microsoft) Storage Spaces Fault Tolerance
  • (Via Microsoft) Better Way To Store Data with EC/LRC
  • (Via Microsoft) Volume resiliency and efficiency in Storage Spaces

Shingled Erasure Code (SHEC)

Shingled Erasure Codes (SHEC) are a variation of Erasure Codes leveraging shingled overlay approach similar to what is being used in Shingled Magnetic Recording (SMR) on some HDDs. Ceph has been an early promoter of SHEC, read more here, and here.

Replication and Mirroring

Replication and Mirroring create a mirror or replica copy of data across different devices, systems, servers, clusters, sites or regions. In addition to keeping a copy, mirror and replication can occur on different time intervals such as real-time ( synchronous) and time deferred (Asynchronous). Besides time intervals, mirror and replication are implemented in different locations at various altitudes or stack layers from lower level hardware adapter or storage systems and appliances, to operating systems, hypervisors, software defined storage, volume managers, databases and applications themselves.

Covered in more detail in chapters 5 and 6, synchronous provides real-time, strong consistency, although high-latency local or remote interfaces can impact primary application performance. Note there is a common myth that high-latency networks are only long distance when in fact some local networks can also be high-latency. Asynchronous (also discussed in more depth in chapters 5 and 6) enables local and remote high-latency communications to be spanned, facilitating protection over a distance without impacting primary application performance, albeit with lower consistency, time deferred, also known as eventual consistency.

Mirroring (also known as RAID 1) and replication creates a copy (a mirror or replica) across two or more storage targets (devices, systems, file systems, cloud storage service, applications such as a database). The reason for using mirrors is to provide a faster (for normal running and during recovery) failure-tolerant mode for enabling availability, resiliency, and data protection, particularly for active data.

Figure 9.10 shows general replication scenarios. Illustrated are two basic mirror scenarios: At the top, a device, volume, file system, or object bucket is replicated to two other targets (i.e., three-way or three replicas); At the bottom, is a primary storage device using a hybrid replica and dispersal technique where multiple data chunks, shards, fragments, or extents are spread across devices in different locations.

SDDC SDDI Mirror and Replication
Figure 9.10 Various Mirror and Replication Approaches

Mirroring and replication can be done locally inside a system (server, storage system, or appliance), within a cabinet, rack, or data center, or remotely, including at cloud services. Mirroring can also be implemented inside a server in software or using RAID and HBA cards to off-load the processing.

SDDC SDDI Mirror Replication Techniques
Figure 9.11 Mirror or Replication combined with Snapshots or other PiT protection

Keep in mind that mirroring and replication by themselves are not a replacement for backups, versions, snapshots, or another recovery point, time-interval (time-gap) protection. The reason is that replication and mirroring maintain a copy of the source at one or more destination targets. What this means is that anything that changes on the primary source also gets applied to the target destination (mirror or replica). However, it also means that anything changed, deleted, corrupted, or damaged on the source is also impacted on the mirror replica (assuming the mirror or replicas were or are mounted and accessible on-line).

implementations in various locations (hardware, software, cloud) include:

  • Applications and databases such as SQL Server, Oracle among others
  • File systems, volume manager, Software-defined storage managers
  • Third-party storage software utilities and drivers
  • Operating systems and hypervisors
  • Hardware adapter and off-load devices
  • Storage systems and appliances
  • Cloud and managed services

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

There are various data protection technologies, tools and techniques for enabling availability of information resources including applications, data and data Infrastructure resources. Likewise there are many different aspects of RAID as well as context from legacy hardware based to cloud, virtual, container and software defined. In other words, not all RAID is in legacy storage systems, and there is a lot of FUD about RAID in general that is probably actually targeted more at specific implementations or products.

There are different approaches to meet various needs from stripe for performance with no protection by itself, to mirror and replication, as well as many parity approaches from legacy to erasure codes including Reed Solomon based as well as LRC among others. Which approach is best depends on your objects including balancing performance, availability, capacity economic (PACE) for normal running behavior as well as during faults and failure modes.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 4 Data Protection Recovery Points (Archive, Backup, Snapshots, Versions).

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Protection Fundamentals Recovery Points (Backup, Snapshots, Versions)

Enabling Recovery Points (Backup, Snapshots, Versions)

Updated 1/7/18

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 4 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 3 Data Protection Access Availability RAID Erasure Codes (EC) including LRC, and click here to view the next post Part 5 Point In Time Data Protection Granularity Points of Interest.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Protection Recovery Points (Archive, Backup, Snapshots, Versions) from Chapter 10 .

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

Enabling RPO (Archive, Backup, CDP, PIT Copy, Snapshots, Versions)

SDDC SDDI Data Protection Points of Interests
Figure 9.5 Data Protection and Availability Points of Interest

RAID, including parity and erasure code (EC) along with mirroring and replication, provide availability and accessibility. These by themselves, however, are not a replacement for backup (or other point in time data protection) to support recovery points. For complete data protection the solution is to combine resiliency technology with point-in-time tools enabling availability and facilitate going back to a previous consistency time.

Recovery point protection is implemented within applications using checkpoint and consistency points as well as log and journal switches or flush. Other places where recovery-point protection occurs include in middleware, database, key-value stores and repositories, file systems, volume managers, and software-defined storage, in addition to hypervisors, operating systems, containers, utilities, storage systems, appliances, and service providers.

In addition to where, there are also different approaches, technologies, techniques, and tools, including archive, backup, continuous data protection, point-in-time copies, or clones such as snapshots, along with versioning.

Common recovery point Data Protection related terms, technologies, techniques, trends and topics pertaining to data protection from availability and access to durability and consistency to point in time protection and security are shown below.

Time interval protection for example with Snapshot, backup/restore, point in time copies, checkpoints, consistency point among other approaches can be scheduled or dynamic. They can also vary by how they copy data for example full copy or clone, or incremental and differential (e.g. what has changed) among other techniques to support 4 3 2 1 data protection. Other variations include how many concurrent copies, snapshots or versions can take place, along with how many stored and for how long (retention).

Additional Data Protection Terms

Copy Data Management ( CDM) as its name implies is associated managing various data copies for data protection, analytics among other activities. This includes being able to identify what copies exist (along with versions), where they are located among other insight.

Data Protection Management ( DPM) as its name implies is the management of data protection from backup/restore, to snapshots and other recovery point in time protection, to replication. This includes configuration, monitoring, reporting, analytics, insight into what is protected, how well it is protected, versions, retention, expiration, disposition, access control among other items.

Number of 9s Availability – Availability (access or durability or access and availability) can be expressed in number of nines. For example, 99.99 (four nines), indicates the level of availability (downtime does not exceed) objective. For example, 99.99% availability means that in a 24-hour day there could be about 9 seconds of downtime, or about 52 minutes and 34 seconds per year. Note that numbers can vary depending on whether you are using 30 days for a month vs. 365/12 days, or 52 weeks vs. 365/7 for weeks, along with rounding and number decimal places as shown in Table 9.1.

Uptime

24-hour Day

Week

Month

Year

99

0 h 14 m 24 s

1 h 40 m 48 s

7 h 18 m 17 s

3 d 15 h 36 m 15 s

99.9

0 h 01 m 27 s

0 h 10 m 05 s

0 h 43 m 26 s

0 d 08 h 45 m 36 s

99.99

0 h 00 m 09 s

0 h 01 m 01 s

0 h 04 m 12 s

0 d 00 h 52 m 34 s

99.999

0 h 00 m 01s

0 h 00 m 07 s

0 h 00 m 36 s

0 d 00 h 05 m 15 s

Table 9.1 Number of 9’s Availability Shown as Downtime per Time Interval

Service Level Objectives SLOs are metrics and key performance indicators (KPI) that guide meeting performance, availability, capacity, and economic targets. For example, some number of 9’s availability or durability, a specific number of transactions per second, or recovery and restart of applications. Service-level agreement (SLA) – SLA specifies various service level objectives such as PACE requirements including RTO and RPO, among others that define the expected level of service and any remediation for loss of service. SLA can also specify availability objectives as well as penalties or remuneration should SLO be missed.

Recovery Time Objective RTO is how much time is allowed before applications, data, or data infrastructure components need to be accessible, consistent, and usable. An RTO = 0 (zero) means no loss of access or service disruption, i.e., continuous availability. One example is an application end-to-end RTO of 4 hours, meaning that all components (application server, databases, file systems, settings, associated storage, networks) must be restored, rolled back, and restarted for use in 4 hours or less.

Another RTO example is component level for different data infrastructure layers as well as cumulative or end to end. In this scenario, the 4 hours includes time to recover, restart, and rebuild a server, application software, storage devices, databases, networks, and other items. In this scenario, there are not 4 hours available to restore the database, or 4 hours to restore the storage, as some time is needed for all pieces to be verified along with their dependencies.

Data Loss Access DLA occurs when data still exists, is consistent, durable, and safe, but it cannot be accessed due to network, application, or other problem. Note that the inverse is data that can be accessed, but it is damaged. Data Loss Event DLE is an incident that results in loss or damage to data. Note that some context is needed in a scenario in which data is stolen via a copy but the data still exists, vs. the actual data is taken and is now missing (no copies exist). Also note that there can be different granularity as well as scope of DLE for example all data or just some data lost (or damaged). Data Loss Prevention DLP encompasses the activities, techniques, technologies, tools, best practices, and tradecraft skills used to protect data from DLE or DLA.

Point in Time (PiT) such as PiT copy or data protection refers to a recovery or consistency point where data can be restored from or to (i.e., RPO), such as from a copy, snapshot, backup, sync, or clone. Essentially, as its name implies, it is the state of the data at that particular point in time.

Recovery Point Objective RPO is the point in time to which data needs to be recoverable (i.e., when it was last protected). Another way of looking at RPO is how much data you can afford to lose, with RPO = 0 (zero) meaning no data loss, or, for example, RPO = 5 minutes being up to 5 minutes of lost data.

SDDC SDDI RTO RPO
Figure 9.8 Recovery Points (point in time to recover from), and Recovery Time (how long recovery takes)

Frequency refers to how often and on what time interval protection is performed.

4 3 2 1 and 3 2 1 data protection rule
Figure 9.4 Data Protection 4 3 2 1 and 3 2 1 rule

In the context of the 4 3 2 1 rule, enabling RPO is associated with durability, meaning number of copies and versions. Simply having more copies is not sufficient because if they are all corrupted, damaged, infected, or contain deleted data, or data with latent nefarious bugs or root kits, then they could all be bad. The solution is to have multiple versions and copies of the versions in different locations to provided data protection to a given point in time.

Timeline and delta or recovery points are when data can be recovered from to move forward. They are consistent points in the context of what is/was protected. Figure 10.1 shows on the left vertical axis different granularity, along with protection and consistency points that occur over time (horizontal axis). For example, data “Hello” is written to storage (A) and then (B), an update is made “Oh Hello,” followed by (C) full backup, clone, and master snapshot or a gold copy is made.

SDDC SDDI Data Protection Recovery consistency points
Figure 10.1 Recovery and consistency points

Next, data is changed (D) to “Oh, Hello,” followed by, at time-1 (E), an incremental backup, copy, snapshot. At (F) a full copy, the master snapshot, is made, which now includes (H) “Hello” and “Oh, Hello.” Note that the previous full contained “Hello” and “Oh Hello,” while the new full (H) contains “Hello” and “Oh, Hello.” Next (G) data is changed to “Oh, Hello there,” then changed (I) to “Oh, Hello there I’m here.” Next (J) another incremental snapshot or copy is made, date is changed (K) to “Oh, Hello there I’m over here,” followed by another incremental (L), and other incremental (M) made a short time later.

At (N) there is a problem with the file, object, or stored item requiring a restore, rollback, or recovery from a previous point in time. Since the incremental (M) was too close to the recovery point (RP) or consistency point (CP), and perhaps damaged or its consistency questionable, it is decided to go to (O), the previous snapshot, copy, or backup. Alternatively, if needed, one can go back to (P) or (Q).

Note that simply having multiple copies and different versions is not enough for resiliency; some of those copies and versions need to be dispersed or placed in different systems or locations away from the source. How many copies, versions, systems, and locations are needed for your applications will depend on the applicable threat risks along with associated business impact.

The solution is to combine techniques for enabling copies with versions and point-in-time protection intervals. PIT intervals enable recovering or access to data back in time, which is a RPO. That RPO can be an application, transactional, system, or other consistency point, or some other time interval. Some context here is that there are gaps in protection coverage, meaning something was not protected.

A good data protection gap is a time interval enabling RPO, or simply a physical and logical break and the distance between the active or protection copy, and alternate versions and copies. For example, a gap in coverage (e.g. bad data protection gap) means something was not protected.

A protection air or distance gap is having one of those versions and copies on another system, in a different location and not directly accessible. In other words, if you delete, or data gets damaged locally, the protection copies are safe. Furthermore, if the local protection copies are also damaged, an air or distance gap means that the remote or alternate copies, which may be on-line or off-line, are also safe.

Good Data Protection Gaps
Figure 9.9 Air Gaps and Data Protection

Figure 10.2 shows on the left various data infrastructure layers moving from low altitude (lower in the stack) host servers or bare metal (BM) physical machine (PM) and up to higher levels with applications. At each layer or altitude, there are different hardware and software components to protect, with various policy attributes. These attributes, besides PACE, FTT, RTO, RPO, and SLOs, include granularity (full or incremental), consistency points, coverage, frequency (when protected), and retention.

SDDC SDDI Data Protection Granularity
Figure 10.2 Protecting data infrastructure granularity and enabling resiliency at various stack layers (or altitude)

Also shown in the top left of Figure 10.2 are protections for various data infrastructure management tools and resources, including active directory (AD), Azure AD (AAD), domain controllers (DC), group policy objects (GPO) and organizational units (OU), network DNS, routing and firewall, among others. Also included are protecting management systems such as VMware vCenter and related servers, Microsoft System Center, OpenStack, as well as data protection tools along with their associated configurations, metadata, and catalogs.

The center of Figure 10.2 lists various items that get protected along with associated technologies, techniques, and tools. On the right-hand side of Figure 10.2 is an example of how different layers get protected at various times, granularity, and what is protected.

For example, the PM or host server BIOS and UEFI as well as other related settings seldom change, so they do not have to be protected as often. Also shown on the right of Figure 10.2 are what can be a series of full and incremental backups, as well as differential or synthetic ones.

Figure 10.3 is a variation of Figure 10.2 showing on the left different frequencies and intervals, with a granularity of focus or scope of coverage on the right. The middle shows how different layers or applications and data focus have various protection intervals, type of protection (full, incremental, snap, differentials), along with retention, as well as some copies to keep.

SDDC SDDI Data Protection Granularity
Figure 10.3 Protecting different focus areas with various granularities

Protection in Figures 10.2 and 10.3 for the PM could be as simple as documentation of what settings to configure, versions, and other related information. A hypervisors may have changes, such as patches, upgrades, or new drivers, more frequently than a PM. How you go about protecting may involve reinstalling from your standard or custom distribution software, then applying patches, drivers, and settings.

You might also have a master copy of a hypervisors on a USB thumb drive or another storage device that can be cloned, customized with the server name, IP address, log location, and other information. Some backup and data protection tools also provide protection of hypervisors (or containers and cloud machine instances) in addition to the virtual machine (VM), guest operating systems, applications, and data.

The point is that as you go up the stack, higher in altitude (layers), the granularity and frequency of protection increases. What this means is that you may have more frequent smaller protection copies and consistency points higher up at the application layer, while lower down, less frequent, yet larger full image, volume, or VM protection, combining different tools, technology, and techniques.

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

Everything is not the same across different environments, data centers, data infrastructures, applications and their workloads (along with data, and its value). Likewise there are different approaches for enabling data protection to meet various SLO needs including RTO, RPO, RAS, FTT and PACE attributes among others. What this means is that complete data protection requires using different new (and old) tools, technologies, trends, services (e.g. cloud) in new ways. This also means leveraging existing and new techniques, learning from lessons of the past to prevent making the same errors.

RAID (mirror, replicate, parity including erasure codes) regardless of where and how implemented (hardware, software, legacy, virtual, cloud) by itself is not a replacement for backup, they need to be combined with recovery point protection of some type (backup, checkpoint, consistency point, snapshots). Also protection should occur at multiple levels of granularity (device, system, application, database, table) to meet various SLO requirements as well as different time intervals enabling 4 3 2 1 data protection.

Keep in mind what is it that you are protecting, why are you protecting it and against what, what is likely to happen, also if something happens what will its impact be, what are your SLO requirements, as well as minimize impact to normal operating, as well as during failure scenarios. For example do you need to have a full system backup to support recovery of an individual database table, or can that table be protected and recovered via checkpoints, snapshots or other fine-grained routine protection? Everything is not the same, why treat and protect everything the same way?

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 5 Point In Time Data Protection Granularity Points of Interest.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Protection Diaries Fundamental Point In Time Granularity Points of Interest

Data Protection Diaries Fundamental Point In Time Granularity

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 5 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 4 Data Protection Recovery Points (Archive, Backup, Snapshots, Versions), and click here to view the next post Part 6 Data Protection Security Logical Physical Software Defined.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Protection points of granularity, addressing different layers and stack altitude (higher application and lower system level) Chapter 10 . among others.

Point-in-Time Protection Granularity Points of Interest

SDDC SDDI Data Protection Recovery consistency points
Figure 10.1 Recovery and consistency points

Figure 10.1 above is a refresh from previous posts about the role and importance of having various recovery points at different time intervals to enable data protection (and restoration). Building upon figure 10.1, figure 10.5 looks at different granularity of where and how data should be protected. Keep in mind that everything is not the same, so why treat everything the same with the same type of protection?

Figure 10.5 shows backup and Data Protection focus, granularity, and coverage. For example, at the top left is less frequent protection of the operating system, hypervisors, and BIOS, UEFI settings. At the middle left is volume, or device level protection (full, incremental, differential), along with various views on the right ranging from protecting everything, to different granularity such as file system, database, database logs and journals, and operating system (OS) and application software, along with settings.

SDDC SDDI Different Protection Granularity
Figure 10.5 Backup and data protection focus, granularity, and coverage

In Figure 10.5, note that the different recovery point focus and granularity also take into consideration application and data consistency (as well as checkpoints), along with different frequencies and coverage (e.g. full, partial, incremental, incremental forever, differential) as well as retention.

Tip – Some context is needed about object backup and backing up objects, which can mean different things. As mentioned elsewhere, objects refer to many different things, including cloud and object storage buckets, containers, blobs, and objects accessed via S3 or Swift, among other APIs. There are also database objects and entities, which are different from cloud or object storage objects.

Another context factor is that an object backup can refer to protecting different systems, servers, storage devices, volumes, and entities that collectively comprise an application such as accounting, payroll, or engineering, vs. focusing on the individual components. An object backup may, in fact, be a collection of individual backups, PIT copies, and snapshots that combined represent what’s needed to restore an application or system.

On the other hand, the content of a cloud or object storage repository ( buckets, containers, blobs, objects, and metadata) can be backed up, as well as serve as a destination target for protection.

Backups can be cold and off-line like archives, as well as on-line and accessible. However, the difference between the two, besides intended use and scope, is granularity. Archives are intended to be coarser and less frequently accessed, while backups can be more frequently and granular accessed. Can you use a backup for an archive and vice versa? A qualified yes, as an archive could be a master gold copy such as an annual protection copy, in addition to functioning in its role as a compliance and retention copy. Likewise, a full backup set to long-term retention can provide and enable some archive functions.

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

A common theme in this series as well as in my books, webinars, seminars and general approach to data infrastructures, data centers and IT in general is that everything is not the same, why treat it all the same? What this means is that there are differences across various environments, data centers, data infrastructures, applications, workloads and data. There are also different threat risks scenarios (e.g. threat vectors and attack surface if you like vendor industry talk) to protect against.

Rethinking and modernizing data protection means using new (and old) tools in new ways, stepping back and rethinking what to protect, when, where, why, how, with what. This also means protecting in different ways at various granularity, time intervals, as well as multiple layers or altitude (higher up the application stack, or lower level).

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 6 Data Protection Security Logical Physical Software Defined.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Infrastructure Data Protection Diaries Fundamental Security Logical Physical

Data Infrastructure Data Protection Security Logical Physical

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 6 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 5 – Point In Time Data Protection Granularity Points of Interest, and click here to view the next post Part 7 – Data Protection Tools, Technologies, Toolbox, Buzzword Bingo Trends.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Infrastructure and Data Protection security including logical as well as physical from chapter 10 , 13 and 14 among others.

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

There are many different aspects of security pertaining to data infrastructures that span various technology domains or focus areas from higher level application software to lower level hardware, from legacy to cloud an software-defined, from servers to storage and I/O networking, logical and physical, from access control to intrusion detection, monitoring, analytics, audit, monitoring, telemetry logs, encryption, digital forensics among many others. Security should not be an after thought of something done independent of other data infrastructure, data center and IT functions, rather integrated.

Security Logical Physical Software Defined

Physical security includes locked doors of facilities, rooms, cabinets or devices to prevent un-authorized access. In addition to locked doors, physical security also includes safeguards to prevent accidental or intentional acts that would compromise the contents of a data center including data Infrastructure resources (servers, storage, I/O networks, hardware, software, services) along with the applications that they support.

Logical security includes access controls, passwords, event and access logs, encryption among others technologies, tools, techniques. Figure 10.11 shows various data infrastructure security–related items from cloud to virtual, hardware and software, as well as network services. Also shown are mobile and edge devices as well as network connectivity between on-premises and remote cloud services. Cloud services include public, private, as well as hybrid and virtual private clouds (VPC) along with virtual private networks (VPN). Access logs for telemetry are also used to track who has accessed what and when, as well as success along with failed attempts.

Certificates (public or private), Encryption, Access keys including .pem and RSA files via a service provider or self-generated with a tool such as Putty or ssh-keygen among many others. Some additional terms including Two Factor Authentication (2FA), Subordinated, Role based and delegated management, Single Sign On (SSO), Shared Access Signature (SAS) that is used by Microsoft Azure for access control, Server Side Encryption (SSE) with various Key Management System (KMS) attributes including customer managed or via a third-party.

SDDC SDDI Data Protection Security
Figure 10.11 Various physical and logical security and access controls

Also shown in figure 10.11 are encryption enabled at various layers, levels or altitude that can range from simple to complex. Also shown are iSCSI IPsec and CHAP along with firewalls, Active Directory (AD) along with Azure AD (AAD), and Domain Controllers (DC), Group Policies Objects (GPO) and Roles. Note that firewalls can exist in various locations both in hardware appliances in the network, as well as software defined network (SDN), network function virtualization (NFV), as well as higher up.

For example there are firewalls in network routers and appliances, as well as within operating systems, hypervisors, and further up in web blogs platforms such as WordPress among many others. Likewise further up the stack or higher in altitude access to applications as well as database among other resources is also controlled via their own, or in conjunction with other authentication, rights and access control including ADs among others.

A term that might be new for some is attestation which basically means to authenticate and be validated by a server or service, for example, a host guarded server attests with a attestation server. What this means is that the host guarded server (for example Microsoft Windows Server) attests with a known attestation server, that looks at the Windows server comparing it to known good fingerprints, profiles, making sure it is safe to run as a guarded resources.

Other security concerns for legacy and software defined environments include secure boot, shield VMs, host guarded servers and fabrics (networks or clusters of servers) for on-premises, as well as cloud. The following image via Microsoft shows an example of shielded VMs in a Windows Server 2016 environment along with host guarded service (HGS) components ( see how to deploy here).


Via Microsoft.com Guarded Hosts, Shielded VMs and Key Protection Services

Encryption can be done in different locations ranging from data in flight or transit over networks (local and remote), as well as data at rest or while stored. Strength of encryption is determined by different hash and cipher codes algorithms including SHA among others ranging from simple to more complex. The encryption can be done by networks, servers, storage systems, hypervisors, operating systems, databases, email, word and many other tools at granularity from device, file systems, folder, file, database, table, object or blob.

Virtual machine and their virtual disks ( VHDX and VMDK) can be encrypted, as well as migration or movements such as vMotions among other activities. Here are some VMware vSphere encryption topics, along with deep dive previews from VMworld 2016 among other resources here, VMware hardening guides here (NSX, vSphere), and a VMware security white paper (PDF) here.

Other security-related items shown in Figure 10.11 include Lightweight Direct Access Protocol (LDAP), Remote Authentication Dial-In User Service (RADIUS), and Kerberos network authentication. Also shown are VPN along with Secure Socket Layer (SSL) network security, along with security and authentication keys, credentials for SSH remote access including SSO. The cloud shown in figure 10.11 could be your own private using AzureStack, VMware (on-site, or public cloud such as IBM or AWS), OpenStack among others, or a public cloud such as AWS, Azure or Google (among others).

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

There are many different aspects, as well as layers of security from logical to physical pertaining to data centers, applications and associated data Infrastructure resources, both on-premises and cloud. Security for legacy and software defined environments needs to be integrated as part of various technology domain focus areas, as well as across them including data protection. The above is a small sampling of security related topics with more covered in various chapters of SDDI Essentials as well as in my other books, webinars, presentations and content.

From a data protection focus, security needs to be addressed from a physical who has access to primary and protection copies, what is being protected against and where, as well as who can access logically protection copes, as well as the configuration, settings, certificates involved in data protection. In other words, how are you protecting your data protection environment, configuration and deployment. Data protection copies need to be encrypted to meet regulations, compliance and other requirements to guard against loss or theft, accidental or intentional. Likewise access control needs to be managed including granting of roles, security, authentication, monitoring of access, along with revocation.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 7 Data Protection Tools, Technologies, Toolbox, Buzzword Bingo Trends

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

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Data Protection Diaries Tools Technologies Toolbox Buzzword Bingo Trends

Fundamental Tools, Technologies, Toolbox, Buzzword Bingo Trends

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

This is Part 7 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 6 Data Protection Security Logical Physical Software Defined, and click here to view the next post Part 8 Walking The Data Protection Talk What I Do.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Protection related tools, technologies, trends as companion to other posts in this series, as well as across various chapters from the SDDI book.

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

Data Protection Tools, Technologies, Toolbox, Buzzword Bingo Trends

There are many data Infrastructure related topics, technologies, tools, trends, techniques and tips that pertain to data protection, many of which have been covered in this series of posts already, as well as in the SDDI Essentials book, and elsewhere. The following are some additional related data Infrastructure data protection topics, tools, technologies.

Buzzword Bingo is a popular industry activity involving terms, trends, tools and more, read more here, here, and here. The basic idea of buzzword bingo is when somebody starts mentioning lots of buzzwords, buzz terms, buzz trends at some point just say bingo. Sometimes you will get somebody who asks what that means, while others will know, perhaps get the point to move on to what’s relevant vs. talking the talk or showing how current they are on industry activity, trends and terms.

Just as everything is not the same across different environments, there are various size and focus from hyper-scale clouds and managed service providers (MSP) server (and storage along with applications focus), smaller and regional cloud, hosting and MSPs, as well as large enterprise, small medium enterprise (SME), small medium business (SMB), remote office branch office (ROBO), small office home office (SOHO), prosumer, consumer and client or edge. Sometimes you will hear server vs. edge or client focus, thus context is important.

Data protection just like data infrastructures span servers, storage, I/O networks, hardware, software, clouds, containers, virtual, hypervisors and related topics. Otoh, some might view data protection as unique to a particular technology focus area or domain. For example, I once had backup vendor tell me that backups and data protection was not a storage topic, can you guess which vendor did not get recommend for data protection of data stored on storage?

Data gets protected to different target media, mediums or services including HDDs, SSD, tape, cloud, bulk and object storage among others in various format from native to encapsulated in save sets, zips, tar ball among others.

Bulk storage can be on-site, on-premises low-cost tape, disk (file, block or object) as well as off-site including cloud services such as AWS S3 (buckets and objects), Microsoft Azure (containers and blobs), Google among others using various Access ( Protocols, Personalities, Front-end, Back-end) technologies. Which type of data protection storage medium, location or service is best depends on what you are trying to do, along with other requirements.

SDDC SDDI data center data protection toolbox
Data Protection Toolbox

SDDC SDDI Object Storage Architecture
Figure 3.18 Generic Object (and Blob) architecture with Buckets (and Containers)

Object Storage

Before discussing Object Storage lets take a step back and look at some context that can clarify some confusion around the term object. The word object has many different meanings and context, both inside of the IT world as well as outside. Context matters with the term object such as a verb being a thing that can be seen or touched as well as a person or thing of action or feeling directed towards.

Besides a person, place or physical thing, an object can be a software defined data structure that describes something. For example, a database record describing somebody’s contact or banking information, or a file descriptor with name, index ID, date and time stamps, permissions and access control lists along with other attributes or metadata. Another example is an object or blob stored in a cloud or object storage system repository, as well as an item in a hypervisor, operating system, container image or other application.

Besides being a verb, object can also be a noun such as disapproval or disagreement with something or someone. From an IT context perspective, object can also refer to a programming method (e.g. object oriented programming [oop], or Java [among other environments] objects and class’s) and systems development in addition to describing entities with data structures.

In other words, a data structure describes an object that can be a simple variable, constant, complex descriptor of something being processed by a program, as well as a function or unit of work. There are also objects unique or with context to specific environments besides Java or databases, operating systems, hypervisors, file systems, cloud and other things.

SDDC SDDI Object Storage Example
Figure 3.19 AWS S3 Object storage example, objects left and descriptive names on right

The role of object storage (view more at www.objectstoragecenter.com) is to provide low-cost, scalable capacity, durable availability of data including data protection copies on-premises or off-site. Note that not all object storage solutions or services are the same, some are immutable with write once read many (WORM) like attributes, while others non-immutable meaning that they can be not only appended to, also updated to page or block level granularity.

Also keep in mind that some solutions and services refer to items being stored as objects while others as blobs, and the name space those are part of as a bucket or container. Note that context is important not to confuse an object container with a docker, kubernetes or micro services container.

Many applications and storage systems as well as appliances support as back-end targets cloud access using AWS S3 API (of AWS S3 service or other solutions), as well as OpenStack Switch API among others. There are also many open source and third-party tools for working with cloud storage including objects and blobs. Learn more about object storage, cloud storage at www.objectstoragecenter.com as well as in chapters 3, 4, 13 and 14 in SDDI Essentials book.

S3 Simple Storage Service

Simple Storage Service ( S3) is the Amazon Web Service (AWS) cloud object storage service that can be used for bulk and other storage needs. The S3 service can be accessed from within AWS as well as externally via different tools. AWS S3 supports large number of buckets and objects across different regions and availability zones. Objects can be stored in a hierarchical directory structure format for compatibility with existing file systems or as a simple flat name space.

Context is important with data protection and S3 which can mean the access API, or AWS service. Likewise context is important in that some solutions, software and services support S3 API access as part of their front-end (e.g. how servers or clients access their service), as well as a back-end target (what they can store data on).

Additional AWS S3 (service) and related resources include:

Data Infrastructure Environments and Applications

Data Infrastructure environments that need to be protected include legacy, software defined (SDDC, SDDI, SDS), cloud, virtual and container based, as well as clustered, scale-out, converged Infrastructure (CI), hyper-converged Infrastructure (HCI) among others. In addition to data protection related topics already converged in the posts in this series (as well as those to follow), a related topic is Data Footprint Reduction ( DFR). DFR comprises several different technologies and techniques including archiving, compression, compaction, deduplication (dedupe), single instance storage, normalization, factoring, zip, tiering and thin provisioning among many others.

Data Footprint Reduction (DFR) Including Dedupe

There is a long-term relationship with data protection and DFR in that to reduce the impact of storing more data, traditional techniques such as compression and compaction have been used, along with archive and more recently dedupe among others. In the Software Defined Data Infrastructure Essentials book there is an entire chapter on DFR ( chapter 11), as well as related topics in chapters 8 and 13 among others. For those interested in DFR and related topics, there is additional material in my books Cloud and Virtual Data Storage Networking (CRC Press), along with in The Green and Virtual Data Center (CRC Press), as well as various posts on StorageIOblog.com and storageio.com. Figure 11.4 is from Software Defined Data Infrastructure Essentials showing big picture of various places where DFR can be implemented along with different technologies, tools and techniques.

SDDC, SDI, SDDI DFR Dedupe
Figure 11.4 Various points of interest where DFR techniques and technology can be applied

Just as everything is not the same, there are different DFR techniques along with implementations to address various application workload and data performance, availability, capacity, economics (PACE) needs. Where is the best location for DFR that depends on your objectives as well as what your particular technology can support. However in general, I recommend putting DFR as close to where the data is created and stored as possible to maximize its effectiveness which can be on the host server. That however also means leveraging DFR techniques downstream where data gets sent to be stored or protected. In other words, a hybrid DFR approach as a companion to data protection should use various techniques, technologies in different locations. Granted, your preferred vendor might only work in a given location or functionality so you can pretty much guess what the recommendations will be ;) .

Tips, Recommendations and Considerations

Additional learning experiences along with common questions (and answers), appendices, as well as tips can be found here.

General action items, tips, considerations and recommendations include:

    • Everything is not the same; different applications with SLO, PACE, FTT, FTM needs
    • Understand the 4 3 2 1 data protection rule and how to implement it.
    • Balance rebuild performance impact and time vs. storage space overhead savings.
    • Use different approaches for various applications and environments.
    • What is best for somebody else may not be best for you and your applications.
    • You cant go forward in the future after a disaster if you cant go back
    • Data protection is a shared responsibility between vendors, service providers and yourself
    • There are various aspects to data protection and data Infrastructure management

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

There are many different buzzword, buzz terms, buzz trends pertaining to data infrastructure and data protection. These technologies span legacy and emerging, software-defined, cloud, virtual, container, hardware and software. Key point is what technology is best fit for your needs and applications, as well as how to use the tools in different ways (e.g. skill craft techniques and tradecraft). Keep context in mind when looking at and discussing different technologies such as objects among others.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 8 Walking The Data Protection Talk.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

Posted on

Data Protection Diaries Fundamentals Walking The Data Protection Talk

Data Protection Diaries Walking The Data Protection Talk

Companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 8 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Data Protection Tools, Technologies, Toolbox, Buzzword Bingo Trends, and click here to view the next post who’s Doing What ( Toolbox Technology Tools).

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around what I (and Server StorageIO) does for Data Protection besides just talking the talk and is a work in progress that is being updated over time with additional insights.

Walking The Data Protection Talk What I Do

A couple of years back I did the first post as part of the Data Protection Diaries series ( view here), that included the following image showing some data protection needs and requirements, as well as what being done, along with areas for improvement. Part of what I and Server StorageIO does involves consulting (strategy, design, assessment), advising and other influencers activities (e.g. blog, write articles, create reports, webinars, seminars, videos, podcasts) pertaining to data Infrastructure topics as well as data protection.

What this means is knowing about the trends, tools, technologies, what’s old and new, who’s doing what, what should be in the data protection toolbox, as well as how to use those for different scenarios. Its one thing to talk the talk, however I also prefer to walk the talk including eating my own dog food applying various techniques, approaches, tools and technologies discussed.

The following are from a previous Data Protection Diaries post where I discuss my data protection needs (and wants) some of which have evolved since then. Note the image on the left is my Livescribe Echo digital pen and paper tablet. On the right is an example of the digital image created and imported into my computer from the Livescribe. In other words, Im able to protect my hand written notes, diagrams and figures.

Data Protection Diaries Data Protection Diaries Walking The Talk
Via my Livescribe Echo digital pen ( get your Livescribe here at Amazon.com)

My Environment and data protection is always evolving, some based on changing projects, others that are more stable. Likewise the applications along with data are varied after all, everything is not the same. My data protection includes snapshots, replication, mirror, sync, versions, backup, archive, RAID, erasure code among others technologies, tools, and techniques.

Applications range from desktop, office, email, documents, spreadsheets, presentations, video, audio and related items in support of day-to-day activities. Then there are items part of various projects that range from physical to virtual, cloud and container leveraging various tools. This means having protection copies (sync, backup, snapshots, consistency points) of virtual machines, physical machine instances, applications and databases such as SQL Server among many others. Other application workloads include web, word press blog and email among others.

The Server StorageIO environment consists of a mix of legacy on-premises technologies from servers, storage, hardware, software, networks, tools as well as software defined virtual (e.g. VMware, Hyper-V, Docker among others), as well as cloud. The StorageIO data Infrastructure environment consists of dedicated private server (DPS) that I have had for several years now that supports this blog as well as other sites and activity. I also have a passive standby site used for testing of the WordPress based blog on an AWS Lightsail server. I use tools such as Updraft Plus Premium to routinely create a complete data protection view (database, plugins, templates, settings, configuration, core) of my WordPress site (runs on DPS) that is stored in various locations, including at AWS.

Data Protection Diaries Walking The Talk
Some of my past data protection requirements (they have evolved)

Currently the Lightsail Virtual Private Server (VPS) is in passive mode, however plans are to enable it as a warm or active standby fail over site for some of the DPS functions. One of the tools I have for monitoring and insight besides those in WordPress and the DPS are AWS Route 53 alerts that I have set up to monitor endpoints. AWS Route 53 is a handy resource for monitoring your endpoints such as a website, blog among other things and have it notify you, or take action including facilitating DNS fail over if needed. For now, Im simply using Route 53 besides as a secondary DNS as a notification tool.

Speaking of AWS, I have compute instances in Elastic Cloud Compute (EC2) along with associated Elastic Block Storage (EBS) volumes as well as their snapshots. I also have AWS S3 buckets in different regions that are on various tiers from standard to infrequent access (IA), as well as some data on Glacier. Data from my DPS at Bluehost gets protected to a AWS S3 bucket that I can access from AWS EC2, as well as via other locations including Microsoft Azure as needed.

Some on-premises data also gets protected to AWS S3 (as well as to elsewhere) using various tools, for different granularity, frequency, access and retention. After all, everything is not the same, why treat it the same. Some of the data protected to AWS S3 buckets is in native format (e.g. they appear as objects to S3 or object enabled applications), as well as file to file based applications with appropriate tools.

Other data that is also protected to AWS S3 from different data protection or backup tools are stored in vendor neutral or vendor specific save set, zip, tar ball or other formats. In other words, I need the tool or compatible tool that knows the format of the saved data to retrieve individual data files, items or objects. Note that this is similar to storing data on tape, HDDs, SSD or other media in native format vs. in some type of encapsulate save set or other format.

In addition to protecting data to AWS, I also have data at Microsoft Azure among other locations. Other locations include non-cloud based off-site where encrypted removable media is periodically taken to a safe secure place as a master, gold in case of major emergency, ransomeware copy.

Why not just rely on cloud copies?

Simple, I can pull individual files or relatively small amounts of data back from the cloud sometimes faster (or easier) than from on-site copies, let alone my off-site, off-line, air gap copies. On the other hand, if I need to restore large amounts of data, without a fast network, it can be quicker to get the air gap off-line, off-site copy, do the large restore, then apply incremental or changed data via cloud. In other a hybrid approach.

Now a common question I get is why not just do one or the other and save some money. Good point, I would save some money, however by doing the above among other things, they are part of being able to test, try new and different things, gain insight, experience not to mention walk the talk vs. simply talking the talk.

Of course Im always looking for ways to streamline to make my data protection more efficient, as well as effective (along with remove complexity and costs).

  • Everything is not the same, so why treat it all the same with common SLO, RTO, RPO and retention?
  • Likewise why treat and store all data the same way, on the same tiers of technology
  • Gain insight and awareness into environment, applications, workloads, PACE needs
  • Applications, data, systems or devices are protected with different granularity and frequency
  • Apply applicable technology and tools to the task at hand
  • Any data I have in cloud has a copy elsewhere, likewise, any data on-premises has a copy in the cloud or elsewhere
  • I implement the 4 3 2 1 rule by having multiple copies, versions, data in different locations, on and off-line including cloud
  • From a security standpoint, many different things are implemented on a logical as well as physical basis including encryption
  • Ability to restore data as well as applications or image instances locally as well as into cloud environments
  • Leverage different insight and awareness, reporting, analytics and monitoring tools
  • Mix of local storage configured with different RAID and other protection
  • Test, find, fix, remediate improve the environment including leveraging lessons learned

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

Everything is not the same, thats why in my environment I use different technologies, tools and techniques to protect my data. This also means having different RTO, RPO across various applications, data and systems as well as devices. Data that is more important has more copies, versions in different locations as well as occurring more frequently as part of 4 3 2 1 data protection. Other data that does not change as frequently, or time sensitive have alternate RTO and RPO along with corresponding frequency of protection.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series Part 9 who’s Doing What (Toolbox Technology Tools).

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.

Posted on

Data Protection Diaries Fundamentals Who Is Doing What Toolbox Technology Tools

Data Protection Toolbox Whos Doing What Technology Tools

Updated 1/17/2018

Data protection toolbox is a companion to Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Fundamental Server Storage I/O Tradecraft ( CRC Press 2017)

server storage I/O data infrastructure trends

By Greg Schulz – www.storageioblog.com November 26, 2017

This is Part 9 of a multi-part series on Data Protection fundamental tools topics techniques terms technologies trends tradecraft tips as a follow-up to my Data Protection Diaries series, as well as a companion to my new book Software Defined Data Infrastructure Essentials – Cloud, Converged, Virtual Server Storage I/O Fundamental tradecraft (CRC Press 2017).

Software Defined Data Infrastructure Essentials Book SDDC

Click here to view the previous post Part 8 Walking The Data Protection Talk, and click here to view the next post Part 10 Data Protection Resources Where to Learn More.

Post in the series includes excerpts from Software Defined Data Infrastructure (SDDI) pertaining to data protection for legacy along with software defined data centers ( SDDC), data infrastructures in general along with related topics. In addition to excerpts, the posts also contain links to articles, tips, posts, videos, webinars, events and other companion material. Note that figure numbers in this series are those from the SDDI book and not in the order that they appear in the posts.

In this post the focus is around Data Protection who’s Doing What ( Toolbox Technology Tools).

SDDC, SDI, SDDI data infrastructure
Figure 1.5 Data Infrastructures and other IT Infrastructure Layers

who’s Doing What (Toolbox Technology Tools)

SDDC SDDI data center data protection toolbox
Data Protection Toolbox

Note that this post is evolving with additional tools, technologies, techniques, hardware, software, services being added over time along with applicable industry links.

The following are a sampling of some hardware, software, solution and component vendors along with service providers involved with data protection from RAID, Erasure Codes (EC) to snapshots, backup, BC, BR, DR, archive, security, cloud, bulk object storage, HDDs, SSD, tape among others including buzzword (and buzz term trends) bingo. Acronis, Actifio, Arcserve, ATTO, AWS, Backblaze, Barracuda, Broadcom, Caringo, Chelsio (offload), Code42/Crashplan, Cray, Ceph, Cisco, Cloudian, Cohesity, Compuverde, Commvault, Datadog, Datrium, Datos IO, DDN, Dell EMC, Druva, E8, Elastifile, Exagrid, Excelero, Fujifilm, Fujutsu, Google, HPE, Huawei, Hedvig, IBM, Intel, Iomega, Iron Mountain, IBM, Jungledisk, Kinetic key value drives (Seagate), Lenovo, LTO organization, Mangstor, Maxta, Mellanox (offload), Micron, Microsoft (Azure, Windows, Storage Spaces), Microsemi, Nakivo, NetApp, NooBaa, Nexsan, Nutanix, OpenIO, OpenStack (Swift), Oracle, Panasas, Panzura, Promise, Pure, Quantum, Quest, Qumulo, Retrospect, Riverbed, Rozo, Rubrik, Samsung, Scale, Scality, Seagate (DotHill), Sony, Solarwinds, Spectralogic, Starwind, Storpool, Strongbox, Sureline, Swiftstack, Synology, Toshiba, Tintri, Turbonomics, Unitrends, Unix and Linux platforms, Vantara, Veeam, VMware, Western Digital (Amplidata, Tegile and others), WekaIO, X-IO, Zadara and Zmanda among many others.

Note if you dont see yours, or your favorite, preferred or clients listed above or in the data Infrastructure industry related links send us a note for consideration to be included in future updates, or having a link, or sponsor spot pointing to your site added. Feel free to add a non sales marketing pitch to courteous comments to the comment section below.

View additional IT, data center and data Infrastructure along with data protection related vendors, services, tools, technologies links here.

Where To Learn More

Continue reading additional posts in this series of Data Infrastructure Data Protection fundamentals and companion to Software Defined Data Infrastructure Essentials (CRC Press 2017) book, as well as the following links covering technology, trends, tools, techniques, tradecraft and tips.

Additional learning experiences along with common questions (and answers), as well as tips can be found in Software Defined Data Infrastructure Essentials book.

Software Defined Data Infrastructure Essentials Book SDDC

What This All Means

Part of modernizing data protection for various data center and data infrastructure environments is to know the tools, technologies and trends that are part of your data protection toolbox. The other part of modernizing data is protection is knowing the techniques of how to use different tools, technologies to meet various application workload performance, availability, capacity economic (PACE) needs.

Also keep in mind that information services requires applications (e.g. programs) and that programs are a combination of algorithms (code, rules, policies) and data structures (e.g. data and how it is organized including unstructured). What this means is that data protection needs to address not only data, also the applications, configuration settings, metadata as well as protecting the protection tools and its data.

Get your copy of Software Defined Data Infrastructure Essentials here at Amazon.com, at CRC Press among other locations and learn more here. Meanwhile, continue reading with the next post in this series, Part 10 Data Protection Fundamental Resources Where to Learn More.

Ok, nuff said, for now.

Gs

Greg Schulz – Microsoft MVP Cloud and Data Center Management, VMware vExpert 2010-2017 (vSAN and vCloud). Author of Software Defined Data Infrastructure Essentials (CRC Press), as well as Cloud and Virtual Data Storage Networking (CRC Press), The Green and Virtual Data Center (CRC Press), Resilient Storage Networks (Elsevier) and twitter @storageio. Courteous comments are welcome for consideration. First published on https://storageioblog.com any reproduction in whole, in part, with changes to content, without source attribution under title or without permission is forbidden.

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2024 Server StorageIO and UnlimitedIO. All Rights Reserved. StorageIO is a registered Trade Mark (TM) of Server StorageIO.