Big Data … I’m sure you’ve heard the term, but what is it? The cynic in me wants to say there’s no universal definition because the marketers want to keep it nebulous. You know the drill—every product adapts, at least in the marketing literature, to become part of the next big thing. In this case, the next big thing is Big Data.

Although there’s a fair amount of this type of bandwagon adaptation going on, this is far too simplistic to be the entire answer. Indeed, Big Data has grown somewhat holistically over time, driven in part by very large data requirements with extreme availability needs, such as social media Websites or the streaming data measurements taken by medical devices. At the same time, analytics has exploded with newer, more sophisticated tools being delivered for deriving useful observations with sophisticated algorithms on large data sets.

Now couple these trends with the NoSQL database movement and advanced analytics, and we see the makings of a meme ... a Big Data meme! But what exactly is Big Data? Forrester Research defines Big Data in the context of what it calls the 4 V’s: Volume, Velocity, Variety, and Variability.

The first V is Volume and that’s the obvious one, right? In order for “data” to be “big,” you must have a lot of it. And most of us do in some form or another. A recent survey published by IDC claims that the volume of data under management by the year 2020 will be 44 times greater than what was managed in 2009.

But Volume is only the first dimension of the Big Data challenge. Velocity refers to the increased speed of data arriving in our systems along with the growing number and frequency of business transactions being conducted. Variety refers to the increasing growth in both structured and unstructured data being managed. Variability refers to the increasing variety of data formats (as opposed to just relational data). Others have tried to add more V’s to the Big Data definition as well. I’ve seen and heard people add Verification, Value, Veracity, and Vicinity to this discussion.

Frequently, Big Data is coupled with NoSQL database systems. The biggest difference between a NoSQL Database Management System (DBMS) and a relational DBMS is that NoSQL doesn’t rely on SQL for accessing data. Additionally, a NoSQL DBMS typically doesn’t require a fixed table schema, doesn’t provide the ACID properties of Atomicity, Consistency, Isolation, and Durability (instead delivering “eventually consistent” data), and is highly scalable. There are no hard-and-fast rules as to how NoSQL databases store data. Some of the more popular NoSQL storage mechanisms include key-value stores, graph databases, and document stores.

Stream computing is another concept that gets tied into Big Data. Stream computing involves the ingestion of data (structured or unstructured) from arbitrary sources and the processing of it without necessarily persisting it. Any digitized data is fair game for stream computing. As the data streams, it’s analyzed and processed in a problem-specific manner. Stream computing is adopted in situations where data is difficult for humans to interpret easily and is likely to be too voluminous to be stored in a database. Examples of types of data include healthcare and stock trades.

The final aspect of Big Data is data analytics. We’re storing all this data for a purpose. By analyzing large amounts of data and looking for trends, patterns, and “interesting” data, analytics can discover issues and solve problems that weren’t practical, or even possible, using traditional computing methods.

So what’s Big Data? We’ve talked about a lot of different things but we haven’t really pinned down a definition yet. Personally, I think all this talk about V’s and NoSQL just muddies the water. To me, Big Data is so simple it needs no definition. It’s like saying Big Dog … you immediately know what I’m talking about. Big Data is all about a lot of data. Big Data doesn’t have to be NoSQL. And you don’t have to sit there counting up your V’s to see if you’re doing it. Real-time analytics on large relational data warehouses qualifies as Big Data to me. And it should to you, too.

As a data bigot, I see the Big Data trend as a good thing. A lot of the more recent computing trends have been process-orientated (e.g., object-oriented programming, Web services, SOA). But the data is more important than the code, and it always will be. As I’ve said before, applications are temporary, but data is forever! And if the Big Data trend helps us better protect, administer, and use our data, then I’m all in favor of it.

Although I’m usually skeptical of marketing trends and industry memes, this one is different. We can use the rise of Big Data to the forefront of computing as a means to improve data quality, institute data governance, and pay more attention to our data management infrastructure. After all, if you’re going to have Big Data, it had better be good Big Data. Big Data Forever!