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The Kognitio on Hadoop Platform, an in-memory parallelization engine, is part of a trend of marrying data warehousing technology with Hadoop to pick up where Hadoop leaves off, to query massive volumes of data and support high concurrency.


  • Limitations of BI and visualization tools, combined with the growing pool of concurrent enterprise analytics users, means that technology for running SQL on Hadoop is needed more than ever to unlock the business value of data stored in Hadoop.
  • The Kognitio on Hadoop platform, which shares its code base with the original Kognitio Analytical Platform, offers the same massively parallel processing which enables ultra-fast SQL access to data for high-concurrency use of BI and visualization tools.
  • Kognitio on Hadoop, with full YARN integration, now allows the Kognitio platform to share hardware infrastructure with other Hadoop applications. The business can leverage its existing investment in Hadoop infrastructure to deploy Kognitio’s in-memory parallelization engine.

Features and Benefits

  • Assesses the limitations of BI and visualization tools when faced with growing numbers of concurrent users and escalating volumes of data.
  • Identifies key issues with the Hadoop ecosystem that have led to questionable ROI and under-utilization in business analytics.
  • Identifies technical aspects of the Kognitio on Hadoop platform which allow it to facilitate BI and visualization at scale.
  • Compares Kognitio on Hadoop to several competing options that offer SQL on Hadoop technology

Key questions answered

  • Why is there a market perception that Hadoop has failed to deliver its full potential in terms of ROI?
  • What limitations do BI and visualization tools face in the self-service era, where there are growing numbers of concurrent users?
  • How does Kognitio on Hadoop accelerate the query process and make data in Hadoop available to more users?
  • Why should the enterprise consider Kognitio on Hadoop, given the availability of competing open source options?
  • Why will the growth of the self-service analytics trend continue to drive the need for SQL on Hadoop technology?

Table of contents

Ovum view

  • Summary
  • It's time for Hadoop to start earning its keep (with a little help)
  • Same processing, same scale, now directly on Hadoop
  • Preparing for the next million analytics users


  • Further reading
  • Author

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