I was recently asked the question that is the title of this blog post and I thought, hmmm, now there’s an interesting topic for my blog. So after answering I wrote down some of my thoughts and I have assembled them here to share with you today. If you have any further thoughts on this topic, please share them in the comments area below!
So, how might life change for DBAs as organizations embrace Big Data? That’s a loaded question. Life is always changing for DBAs! The DBA is at the center of new application development and therefore is always learning new technologies – not always database-related technologies. Big Data will have a similar impact. There is a lot of new technology to learn. Of course, not every DBA will have to learn each and every type of technology.
DBAs should be learning NoSQL DBMS technologies, but not with an eye toward replacing relational. Instead, at least for the time-being, NoSQL technologies (Key/Value, column, document store, and graph) are currently very common in big data and advanced analytics projects. My view is that these products will remain niche solutions, but the technology will be widely adopted. How will that happen? Well, relational DBMSs will add functionality to combat the NoSQL offerings, just like they did to combat the Object-Oriented DBMS offerings in the 1990s. So instead of just offering a relational engine, a DBMS (such as Oracle or DB2) will offer additional engines, such as key/value or document stores.
That means that DBAs who spend the time to learn what the NoSQL database technologies do today will be well-prepared for the multi-engine DBMS of the future. Not only will the NoSQL-knowledgeable DBA be able to help implement projects where organizations are using NoSQL databases today, but they will also be ahead of their peers when NoSQL functionality is added to their RDBMS product(s).
DBAs should also learn about Hadoop, MapReduce and Spark. Now Hadoop is not a DBMS, but it is likely to be a long-term mainstay for data management, particularly for managing big data. An education in Hadoop and MapReduce will bolster a DBA’s career and make them more employable long-term. And Spark looks like it is here for the long run, too. So learning how Spark can speed up big data requests with in-memory capabilities is also a good career bet.
It would also be a good idea for DBAs to read up on analytics and data science. Although most DBAs will not become data scientists, some of their significant users will be. And learning what your users do – and want to do with the data – will make for a better DBA.
And, of course, a DBA should be able to reasonably discuss what is meant by the term “Big Data.” Big Data is undoubtedly here to stay. Of course, the industry analyst firms have come up with their definitions of what it means to be processing “Big Data”, the most famous of which talks about “V”s. As interesting as these definitions may be, and as much discussion as they create, the definitions don’t really help to define what benefit organizations can glean from Big Data.
So, with that in mind, and if we wanted to be more precise, it would probably make sense to talk about advanced analytics, instead of Big Data. Really, the analytics is the motivating factor for Big Data. We don’t just store or access a bunch of data because we can… we do it to learn something that will give us a business advantage… that is what analytics is. Discovering nuggets of reality in mounds and mounds of data. But I am not in favor of that. Why?
Well, more than half the battle is getting the attention of decision makers, and the term Big Data has that attention in most organizations. As a data proponent, I think that the data-focused professionals within companies today should be trying to tie all of the data management and exploitation technologies to the Big Data meme in order to the attention of management and be able to procure funding. C’mon, as a DBA doesn’t it make sense to take advantage of an industry meme with the word “data” right in it? By doing so we can better manage the data (small, medium and big) that we are called upon to manage!
Finally, I would urge DBAs to automate as many data management tasks as possible. The more automated existing management tasks become, the more available DBAs become to learn about, and work on the newer, more sexy projects.