Hadoop

While there are plenty out there who think of Sears when they think of dress slacks or hardware, they may not think of data warehousing. But behind the scenes of Sears, it may well be onto something, specifically in the way it’s using Hadoop in its systems.

Sears started using Hadoop back in 2009, but over the intervening three years, it became a major part of theirits operations, extending so far as to be described as "the central hub of all data management activity for the retailer.” What's really driving the use of Hadoop at Sears is the way it can not only handle substantial amounts of data more efficiently--and thus more cost-effectively--than the standard relational database, but also the way it can work with both simple and complex data alike without the need to pre-sort that data, as is commonly done when using a database. The necessary schema for the data can be applied as it's needed, rather than establishing the schema before the data is even loaded.

Better yet, Hadoop also handles a lot of Sears' data analysis, thanks to Hadoop's MapReduce processing capabilities. Though Sears still uses relational databases, especially the InfoBright columnar database, even here Hadoop has a hand in things, as thanks to Hadoop, cube building for InfoBright no longer needs to take place and fresh data sets can go right from Hadoop to InfoBright.

Hadoop also helps Sears power its Teradata (News - Alert) systems as well. Though Sears is using more Teradata than ever, part of that is because Hadoop helps Sears to store and retain more data than ever, giving Teradata more to work with. Sears' CTO, Phil Shelley, explained the value of having Hadoop on hand: "We keep all the raw, transactional data, and because there's enough horsepower in Hadoop, you can then transform it into any form you want whenever you want on they fly rather than having to create cubes or aggregations."

More and more, retailers are discovering the extreme importance of having data management systems on hand. Determining stock levels, noticing trends in purchases, and properly serving customers hinges on not only having access to the correct data, but also being able to manipulate that data so that it can be easily interpreted and condensed into actionable forms. Data is useless unless it can be understood, and that's part of what Hadoop can do at the enterprise level.

It's great to compile data, but making data useful is the end result--or should be the end result--of that compiling. Failing to use data correctly is just as bad--worse even, when the wasted time, effort and resources are considered--as not gathering data at all. Sears may well be showing the rest of the business world the future of data warehousing and how to make all that data useful, thanks to Hadoop and several other programs.

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