Accelerate Amazon EMR for Spark & More

Accelerate Amazon EMR for Spark & More

Aug 19, 2021

Amazon EMR is growing in popularity, and is emerging as the leading platform for big data processing on AWS. EMR is the preferred platform for “lift and shift” migration of existing Hadoop and Spark workloads to the cloud, with minimal refactoring. You get better control, enhanced flexibility, and greater responsiveness.

Would your organization benefit from rapid troubleshooting and performance optimization for your Amazon EMR workloads? If you’re running significant workloads on Amazon EMR then you may be looking for ways to get faster performance, and meet SLAs, without excessive resource use and cost. You will want to find the equivalents to the approaches you used on-premises, plus cloud-specific ways to get the job(s) done, faster.

Join Chris Santiago, Director of Solutions Engineering at Unravel Data to see how Unravel can deliver:

  • AI-powered recommendations and automated actions to enable intelligent optimization of your big data pipelines and applications.
  • End-to-end monitoring, measurement, and troubleshooting of apps using Spark, Hadoop, Kafka, and related technologies.
  • Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Amazon EMR environment.

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