Application Performance Management for Apache Spark
Optimize, troubleshoot and analyze Apache Spark performance for all applications on the Apache Spark Core
SCHEDULE A DEMO
From data transformation and SQL applications to real-time streaming applications and data pipelines
powered by AI and machine learning, Spark has made it easier than ever to create big data applications.
However, moving these applications into production and running them in production continuously and
reliably is challenging. Learn how you can simplify your Spark application development and operations
management with Unravel.
Learn how to run Spark in production reliably
Learn how to reduce Spark troubleshooting time from days to seconds
Many new big data applications are being built with Spark in fields like healthcare, genomics, financial services,
self-driving technology, government, and media. Things are not so rosy, however, when a Spark application fails.
Learn how Unravel can radically simplify root cause detection of any Spark application failure by
automatically providing insights to Spark users
Learn how to gain 5x speed up for Spark jobs
Learn how to reduce the number of tasks for queries to save resources,
but also drastically improves query speedup.
Manage all your applications on the Apache Spark Core
Optimize applications and pipelines
Detect and fix inefficient and failed applications
Troubleshoot multi-system pipelines from a single location
Ensure compliance on reliability, throughput, and response-time SLAs
Get powerful insights into data usage and access
Ensure optimal use of in-memory data caching
Optimize HDFS, NoSQL, and Kafka usage for Spark
Detect and fix poor partitioning
Optimize your big data resources
Optimize container sizes for Spark on Mesos and YARN
Get instructions for tuning JVM for Spark drivers and executors
Minimize data shuffles
See how StitchFix solves problems with their Apache Spark applications