Which freaking database should I use?

In the era of big data, good old RDBMS is no longer the right tool for many database jobs. Here's a quick guide to choosing among NoSQL alternatives

By Andrew Oliver, InfoWorld |  Big Data, databases, nosql

I've been in Chicago for the last few weeks setting up our first satellite office for my company. While Silicon Valley may be the home of big data vendors, Chicago is the home of the big data users and practitioners. So many people here "get it" that you could go to a packed meetup or big data event nearly every day of the week.

Big data events almost inevitably offer an introduction to NoSQL and why you can't just keep everything in an RDBMS anymore. Right off the bat, much of your audience is in unfamiliar territory. There are several types of NoSQL databases and rational reasons to use them in different situations for different datasets. It's much more complicated than tech industry marketing nonsense like "NoSQL = scale."

[ Andrew Oliver declares the time for NoSQL standards is now. | Also on InfoWorld: NoSQL standouts: New databases for new applications | Get a digest of the key stories each day in the InfoWorld Daily newsletter. ]

Part of the reason there are so many different types of NoSQL databases lies in the CAP theorem, aka Brewer's Theorem. The CAP theorem states you can provide only two out of the following three characteristics: consistency, availability, and partition tolerance. Different datasets and different runtime rules cause you to make different trade-offs. Different database technologies focus on different trade-offs. The complexity of the data and the scalability of the system also come into play.

Another reason for this divergence can be found in basic computer science or even more basic mathematics. Some datasets can be mapped easily to key-value pairs; in essence, flattening the data doesn't make it any less meaningful, and no reconstruction of its relationships is necessary. On the other hand, there are datasets where the relationship to other items of data is as important as the items of data themselves.


Originally published on InfoWorld |  Click here to read the original story.
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