3 Big Data Challenges: Expert AdviceShare The Results
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For now, many practitioners and vendors are content to let SQL and NoSQL systems coexist. Most data warehousing platforms and many business intelligence suites now offer integration with Hadoop. So practitioners can do their large-scale MapReduce or data-transformation work in Hadoop, then move result sets into more familiar and accessible data warehousing and BI tools. An Internet marketing firm might use MapReduce to spot Web sessions relevant to an ad campaign from huge volumes of clickstream data, then bring that result set into an SQL environment for segmentation or predictive analysis.
Online retailer Ideeli is applying this blended approach, using Hadoop to store and process large volumes of Web log clickstream and email campaign data and using Pentaho for BI.
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The company sets up members-only "flash sale" sites where it sells small quantities of high-fashion items, fueled by email and social media promotions. The sales typically last a day or two before the inventory is gone, and the boutique is taken offline. Ideeli studies Web traffic to understand which of its 5 million members are responding to a campaign, the traits of lookers versus buyers, and so on.
The trouble with an all-Hadoop approach, Ideeli found, was that Apache Hive--the data summarization, query, and analysis tool that runs on top of Hadoop--was too slow, taking several minutes to handle demanding queries, says Paul Zanis, director of data services at Ideeli. The choice of Pentaho for BI is perhaps no surprise, given that Pentaho has support for Hadoop, including the ability to design MapReduce jobs, extract data from Hadoop, and support scheduled reporting and ad hoc analysis from Hadoop tools.
Ideeli is still building the data warehouse it needs to support the new approach, but the idea is to use Pentaho's data-integration software to extract and transform end-of-day batch loads of clickstream and campaign data. From there, Pentaho's OLAP capabilities will automatically generate new cubes for rapid analysis.
"Once that's in place, we'll be able to explore high-level, summarized data within seconds versus trying to run a Hive query, which would take several minutes," Zanis says.
But there's a limitation today on Hadoop and other NoSQL environments: scarce expertise. Schools, vendors, and companies have spent decades teaching SQL, but Hadoop software distributions have only been available since 2009. Efforts like EMC's Hadoop initiatives are aimed at making it easier to deploy and manage big-data-oriented relational and Hadoop environments side by side, but you'll still need Hadoop expertise to deploy and manage that separate environment. Until these platforms gain larger pools of expertise, data management pros will have to find ways to deliver results through fast and familiar tools.