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Eliminate Hadoop Data Silos and Bottlenecks

Hadoop applications built on traditional HDFS and direct-attached storage (DAS) architectures are leading to a sprawl of data silos, redundant data copies and an administrative nightmare of poorly utilized resources. As a result, even the most agile analytics and IT teams are starting to hit a complexity wall and struggle to eliminate the bottlenecks that slow down the build-out of new data pipelines and machine learning projects.

Join our senior analytics specialists for a technical deep-dive into the latest architectural designs and shared data strategies that address today’s data lake challenges, including how to

  • Migrate between analytics platforms seamlessly without having to reload datasets.
  • Reduce multiple copies of data.
  • Lower memory footprint.
  • Configure Ambari to leverage a shared data hub architecture.
  • Leverage advanced strategies and tools for Hadoop, Hive and Spark.

 

2019-10-04T14:49:00.000+05:30

Miroslav Klivansky

Field Solution Architect, Pure Storage

Mark Medovich

System Engineer, Pure Storage
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