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Why Aren’t BI Users Analyzing Hadoop Data? – Part 2

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Why Aren’t BI Users Analyzing Hadoop Data? – Part 2

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Continuing from part 1 of this blog series, we will focus on answer the question “Who are some of the folks working to make Hadoop more accessible to BI users?”

There are multiple Apache Hadoop projects in various stages of maturity to make access to data easier and better.  They include Map Reduce, Hive, Pig, and an alphabet soup of others.

At the same time, Hadoop distribution vendors are each working on their own enhancements.  Cloudera’s Impala is designed to enable real time, high performance queries against data stored in HDFS or Hbase using SQL syntax.  Hortonworks’ Stinger project is designed to dramatically speed up Hive performance and provide more functionality.  EMC has integrated its Greenplum MPP database with its Hadpop distribution (called Pivotal) to provide high performance SQL queries with Hadoop.  I’m sure the other Hadoop distro vendors are doing similar things as well.

All of the more established BI vendors, including IBM, Microsoft, Microstrategy, Oracle, and SAP are deepening their Hadoop integration solutions as a new generation of market entrants such as Actian, Alteryx, Datameer, Karmasphere, Platfora and others are introducing a range of diverse and innovative Hadoop-based analytics tools.

As in any early stage IT market segment, there’s currently a great deal of complex technical jargon, nuanced functionality, and rapid fire advancements that customers will need to keep up with in selecting the right solutions for their needs.  BI and analytics tool vendors introducing new or enhanced solutions for Hadoop need to market to their audience using language that is simple to understand, technically credible, and clearly differentiated.

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