Unravel makes Spark perform better and more reliably. Unravel's Spark application tuning tool complements Spark web UI and automatically troubleshoots and tunes your Spark jobs. Avoid errors, slowdowns, and costly resource usage. Unravel provides deep insights and intelligence for Spark pipelines and jobs running on Databricks, Amazon EMR, Cloudera, Google Dataproc, Azure HDInsight and Apache open source Spark versions.
The Unravel application for Spark tuning provides a comprehensive full-stack, intelligent, and automated approach to Spark operations and performance management on your modern data architecture. The Unravel platform helps you analyze, troubleshoot, and optimize Spark queries, applications, and pipelines within a seamless, intuitive user experience.
Run Spark like a boss with a tool that’s more than just a Spark apm.
Less time troubleshooting and tuning Spark applications; more time for everything else.
Unravel’s platform is so much more than just a Spark apm tool. It uses advanced analytical techniques, machine learning models, and a built-in AI engine to provide insights, recommendations, and auto-tuning for Spark applications and pipelines. Unravel enables:
Get wide and deep
End-to-end Spark observability means you don’t miss anything.
Unravel automatically links your Spark jobs to the pipeline that they run under. Unravel also connects other services, data sources, containers, and hosts, giving you end-to-end observability. This allows you to:
Supercharge your data environment
Unravel is easy to set up and integrates with all your favorite tools.
Unravel works with all your Spark code – Java, Scala, R, Python – Spark Streaming, SparkSQL, MLib, and GraphX: