Troubleshooting Amazon EMR
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.
However, as the importance of EMR grows, so does the importance of reliability for EMR jobs - especially big data jobs such as Spark workloads. Information you need for troubleshooting is scattered across multiple, voluminous log files. The right log files can be hard to find, and even harder to understand. There are other tools, each providing part of the picture, leaving it to you to try to assemble the jigsaw puzzle yourself.
Would your organization benefit from rapid troubleshooting for your EMR workloads? If you’re running significant workloads on EMR, then you may be looking for ways to find and fix problems faster and better - and to find new approaches that steadily reduce your problems over time. You will want to find equivalents to the approaches you used on-premises, plus cloud-specific ways to fix jobs, faster.
Join Mike Wong, Solutions Engineer at Unravel Data, to see how Unravel can deliver:
- Enhanced observability through the use of additional sensors, placed in the JVM, plus intelligent curation and presentation of existing log and other data
- End-to-end monitoring, measurement, and troubleshooting of apps using Spark and related technologies.
- AI-powered recommendations and automated actions to enable pre-emptive fixes of problems with your big data pipelines and applications.
- Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Spark environment.
Learn More About Unravel Data
Try Unravel for free: https://www.unraveldata.com/saas-free-trial/