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.