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It seems obvious that given the decreasing cost of storage and computation, there's going to be a significant increase in the volume of data that organizations accumulate over the next 10 years. But the type of data being accumulated may be different from the areas where traditional DBMSs dominated. It's not just about transactions; it's search patterns, on-line behavior, click-thru data, events fired off by smartphones, messages over Twitter & Facebook, log data of various kinds.
If an organization can figure out a better way identify prospects, or deliver more targeted ads, or optimize pricing decisions by analyzing terrabytes of data, they'd be crazy not to. Over the long term, companies that don't develop these capabilities will be at a competitive disadvantage.
As to what the implications are from a technological perspective, that's a whole different can of worms. I'm starting to see adoption of Big Data technologies like Hadoop, HDFS, Cassandra, MongoDB, XML databases, analysis with R, Pentaho, and loads of other technologies. And MySQL continues to play a role here as do other traditional relational databases. Over the next few months, I'm going to dig down deeper with people using these technologies to try and discern the emerging customer patterns.
If you're in this space or using some of these technologies, let me know your thoughts. What volume of data are you dealing with? How many nodes or servers are you using? Are you running on a public cloud, private cloud or hybrid? What technologies did you evaluate? What about traditional DBMSs didn't work for this scenario?
- Wikipedia: R, Hadoop, Cassandra, MongoDB
- Web sites: Cran, Hadoop, Cassandra, MongoDB
- Companies: Pentaho, Revolution Analytics, Cloudera, CouchOne, 10Gen, NorthScale, Riptano
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