Chris Santiago, Director of Solutions Engineering, Unravel
Apache Spark is the leading technology for big data processing, on-premises and in the cloud. Spark powers advanced analytics, AI, machine learning, and more. Spark provides a unified infrastructure for all kinds of professionals to work together to achieve outstanding results. Technologies such as Cloudera’s offerings, Amazon EMR, and Databricks are largely used to run Spark jobs. However, as Spark’s importance grows, so does the importance of Spark reliability - and troubleshooting Spark problems is hard. 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 Spark workloads? If you’re running significant workloads on Spark, 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. 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, Hadoop, Kafka, 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.
Don’t wait. Register today for this informative and actionable webinar.