This is an industry trends and perspective piece about big data and little data, industry adoption and customer deployment.
If you are in any way associated with information technology (IT), business, scientific, media and entertainment computing or related areas, you may have heard big data mentioned. Big data has been a popular buzzword bingo topic and term for a couple of years now. Big data is being used to describe new and emerging along with existing types of applications and information processing tools and techniques.
I routinely hear from different people or groups trying to define what is or is not big data and all too often those are based on a particular product, technology, service or application focus. Thus it should be no surprise that those trying to police what is or is not big data will often do so based on what their interest, sphere of influence, knowledge or experience and jobs depend on.
Not long ago while out traveling I ran into a person who told me that big data is new data that did not exist just a few years ago. Turns out this person was involved in geology so I was surprised that somebody in that field was not aware of or working with geophysical, mapping, seismic and other legacy or traditional big data. Turns out this person was basing his statements on what he knew, heard, was told about or on sphere of influence around a particular technology, tool or approach.
Fwiw, if you have not figured out already, like cloud, virtualization and other technology enabling tools and techniques, I tend to take a pragmatic approach vs. becoming latched on to a particular bandwagon (for or against) per say.
Not surprisingly there is confusion and debate about what is or is not big data including if it only applies to new vs. existing and old data. As with any new technology, technique or buzzword bingo topic theme, various parties will try to place what is or is not under the definition to align with their needs, goals and preferences. This is the case with big data where you can routinely find proponents of Hadoop and Map reduce position big data as aligning with the capabilities and usage scenarios of those related technologies for business and other forms of analytics.
Not surprisingly the granddaddy of all business analytics, data science and statistic analysis number crunching is the Statistical Analysis Software (SAS) from the SAS Institute. If these types of technology solutions and their peers define what is big data then SAS (not to be confused with Serial Attached SCSI which can be found on the back-end of big data storage solutions) can be considered first generation big data analytics or Big Data 1.0 (BD1 ;) ). That means Hadoop Map Reduce is Big Data 2.0 (BD2 ;) ;) ) if you like, or dislike for that matter.
Funny thing about some fans and proponents or surrogates of BD2 is that they may have heard of BD1 like SAS with a limited understanding of what it is or how it is or can be used. When I worked in IT as a performance and capacity planning analyst focused on servers, storage, network hardware, software and applications I used SAS to crunch various data streams of event, activity and other data from diverse sources. This involved correlating data, running various analytic algorithms on the data to determine response times, availability, usage and other things in support of modeling, forecasting, tuning and trouble shooting. Hmm, sound like first generation big data analytics or Data Center Infrastructure Management (DCIM) and IT Service Management (ITSM) to anybody?
Now to be fair, comparing SAS, SPSS or any number of other BD1 generation tools to Hadoop and Map Reduce or BD2 second generation tools is like comparing apples to oranges, or apples to pears.
Lets move on as there is much more to what is big data than simply focus around SAS or Hadoop.
Another type of big data are the information generated, processed, stored and used by applications that result in large files, data sets or objects. Large file, objects or data sets include low resolution and high-definition photos, videos, audio, security and surveillance, geophysical mapping and seismic exploration among others. Then there are data warehouses where transactional data from databases gets moved to for analysis in systems such as those from Oracle, Teradata, Vertica or FX among others. Some of those other tools even play (or work) in both traditional e.g. BD1 and new or emerging BD2 worlds.
This is where some interesting discussions, debates or disagreements can occur between those who latch onto or want to keep big data associated with being something new and usually focused around their preferred tool or technology. What results from these types of debates or disagreements is a missed opportunity for organizations to realize that they might already be doing or using a form of big data and thus have a familiarity and comfort zone with it.
By having a familiarity or comfort zone vs. seeing big data as something new, different, hype or full of FUD (or BS), an organization can be comfortable with the term big data. Often after taking a step back and looking at big data beyond the hype or fud, the reaction is along the lines of, oh yeah, now we get it, sure, we are already doing something like that so lets take a look at some of the new tools and techniques to see how we can extend what we are doing.
Likewise many organizations are doing big bandwidth already and may not realize it thinking that is only what media and entertainment, government, technical or scientific computing, high performance computing or high productivity computing (HPC) does. I’m assuming that some of the big data and big bandwidth pundits will disagree, however if in your environment you are doing many large backups, archives, content distribution, or copying large amounts of data for different purposes that consume big bandwidth and need big bandwidth solutions.
Yes I know, that’s apples to oranges and perhaps stretching the limits of what is or can be called big bandwidth based on somebody’s definition, taxonomy or preference. Hopefully you get the point that there is diversity across various environments as well as types of data and applications, technologies, tools and techniques.
What about little data then?
I often say that if big data is getting all the marketing dollars to generate industry adoption, then little data is generating all the revenue (and profit or margin) dollars by customer deployment. While tools and technologies related to Hadoop (or Haydoop if you are from HDS) are getting industry adoption attention (e.g. marketing dollars being spent) revenues from customer deployment are growing.
Where big data revenues are strongest for most vendors today are centered around solutions for hosting, storing, managing and protecting big files, big objects. These include scale out NAS solutions for large unstructured data like those from Amplidata, Cray, Dell, Data Direct Networks (DDN), EMC (e.g. Isilon), HP X9000 (IBRIX), IBM SONAS, NetApp, Oracle and Xyratex among others. Then there flexible converged compute storage platforms optimized for analytics and running different software tools such as those from EMC (Greenplum), IBM (Netezza), NetApp (via partnerships) or Oracle among others that can be used for different purposes in addition to supporting Hadoop and Map reduce.
If little data is databases and things not generally lumped into the big data bucket, and if you think or perceive big data only to be Hadoop map reduce based data, then does that mean all the large unstructured non little data is then very big data or VBD?
Of course the virtualization folks might want to if they have not already corner the V for Virtual Big Data. In that case, then instead of Very Big Data, how about very very Big Data (vvBD). How about Ultra-Large Big Data (ULBD), or High-Revenue Big Data (HRBD), granted the HR might cause some to think its unique for Health Records, or Human Resources, both btw leverage different forms of big data regardless of what you see or think big data is.
Does that then mean we should really be calling videos, audio, PACs, seismic, security surveillance video and related data to be VBD? Would this further confuse the market, or the industry or help elevate it to a grander status in terms of size (data file or object capacity, bandwidth, market size and application usage, market revenue and so forth)?
Do we need various industry consortiums, lobbyists or trade groups to go off and create models, taxonomies, standards and dictionaries based on their constituents needs and would they align with those of the customers, after all, there are big dollars flowing around big data industry adoption (marketing).
What does this all mean?
Is Big Data BS?
First let me be clear, big data is not BS, however there is a lot of BS marketing BS by some along with hype and fud adding to the confusion and chaos, perhaps even missed opportunities. Keep in mind that in chaos and confusion there can be opportunity for some.
IMHO big data is real.
There are different variations, use cases and types of products, technologies and services that fall under the big data umbrella. That does not mean everything can or should fall under the big data umbrella as there is also little data.
What this all means is that there are different types of applications for various industries that have big and little data, virtual and very big data from videos, photos, images, audio, documents and more.
Big data is a big buzzword bingo term these days with vendor marketing big dollars being applied so no surprise the buzz, hype, fud and more.
Ok, nuff said, for now.
Cheers gs
Greg Schulz – Author Cloud and Virtual Data Storage Networking (CRC Press, 2011), The Green and Virtual Data Center (CRC Press, 2009), and Resilient Storage Networks (Elsevier, 2004)
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