The popularity of Databricks is rocketing skyward, and it is now the leading multi-cloud platform for Spark and analytics workloads, offering fully managed Spark clusters in the cloud. Databricks is fast and organizations generally refactor their applications when moving them to Databricks. The result is strong performance. However, as usage of Databricks grows, so does the importance of reliability for Databricks jobs - especially big data jobs such as Spark workloads. But 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.
Join Patrick Mawyer, Senior Solutions Engineer at Unravel Data, and 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; clear, AI-powered recommendations; and user-specified AutoActions to help you make the most of your Spark environment.
Learn More About Unravel Data Website: https://www.unraveldata.com/
Try Unravel for free: https://www.unraveldata.com/saas-free-trial/