| Monday, November 26, 2012 | |  | | Doug Henschen | | | | | Enterprise data warehouse (EDW) tools and techniques are proven, the SQL query language is well known, and there's plenty of expertise available to keep EDWs humming. But the downside for many warehouses built on relational databases is that they're hard to change because you have to start by schema. When new data sources and new questions arise, the schema and related ETL and BI applications require expensive and time-consuming changes.
Enter Hadoop, which lets you store data on a massive scale at low cost (compared with similarly scaled commercial databases). What's more it easily handles variety, complexity and change because you don't have to conform data to a predefined schema. Sounds great, but where do you find qualified people who know how to use Pig, Hive, Scoop and other tools needed to run Hadoop? And how do you get fast answers out of a batch-oriented platform that relies on slow, iterative MapReduce processing?
Scott Gnau of Teradata and Ben Werther of Platfora square off in an EDW vs. Hadoop debate. Chime in with your comments on the future of data warehousing.
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Doug Henschen
Executive Editor, InformationWeek | |  | | JOIN THE CONVERSATION Posted By Rick Biehl: "Our warehouses are highly generic, with very few fact columns into which we generically load lots of different kinds of data. Queries are driven by the metadata, not the schema design. It takes only a couple of months to implement new data sources, and we never have to change our schema. So it seems to me that our enterprise warehouse already works in a relational form in much the same way as Hadoop is being described here."
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