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Big Data Analytics: The View from Cloudera

Blog by Philip Russom, Research Director for Data Management, TDWI

I recently had a great phone conversation with Mike Olson, the CEO of Cloudera. Mike has a gift for explaining new and complex technologies and their emerging best practices. Let me share a few of Mike’s insights.

Philip Russom: My understanding is that Cloudera makes a business by distributing open source software, namely MapReduce-based Apache Hadoop. Is that right?

Mike Olson: Well, that’s part of it. Cloudera does a lot more than simply distribute open source Hadoop. We make Hadoop viable for serious enterprise users by also providing technical support, upgrades, administrative tools for Hadoop clusters, professional services, training, and Hadoop certification. Furthermore, our distribution package of Hadoop includes more than Hadoop. So Cloudera collects and develops additional components to strengthen and extend Hadoop.

Philip Russom: So, what is Hadoop?

Mike Olson: Essentially there are two pieces in Hadoop. First, there’s the Hadoop Distributed File System (or HDFS), which can manage big data on clusters of many nodes. Our customers typically start with twenty nodes or so, then quickly grow to fifty or more. Some of our customers have thousands of nodes, managing petabytes of data. A many-node cluster enables big data management, plus other nice benefits like scalability, performance, and high availability. But the ramification is that data is heavily distributed.

That’s where the second piece comes in, namely MapReduce. Thanks to this capability of Hadoop, you can define a data operation--like a query or analysis--and the platform ‘maps’ the operation across all relevant nodes, for distributed processing and data collection. The platform then consolidates and reduces the responses that come back. Due to the distributed processing of MapReduce, analytics against very big data is possible—and with good performance.

Philip Russom: What kind of analytics?

Mike Olson: Hadoop excels in discovering patterns in big data, patterns that you didn’t know were there, in data that you probably don’t know very well. That makes Hadoop the opposite of your average data warehouse query against well-understood relational data. Since Hadoop and a traditional data warehouse are complementary, putting them together gives you a very broad range of business intelligence capabilities.

Philip Russom: What data types and data models are your customers managing?

Mike Olson: In Hadoop, you can mix and match data types to your heart’s content. Hadoop will store anything without requiring a data type declaration. Also, Hadoop is amazingly tolerant of messy data. For example, our customers manage any kind of file you can think of in the HDFS, and these can have just about any kind of data model. This also includes human language text and complex data types. So, big data’s not just big. It’s also highly diverse and complicated. And Hadoop excels in handling data of such extreme size, diversity, and complexity for the purposes of analytics.

So, what do you think, folks? Let me know. Thanks!

Posted by Philip Russom, Ph.D. on May 12, 2011


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