Application Recommendations

Developing distributed applications is complex. Developing fast and efficient applications requires an understanding of dozens of performance metrics and tuning parameters. Pepperdata automatically delivers job-specific recommendations based on industry best practices and in-depth knowledge of those performance metrics and tuning parameters.

Change configuration parameters to optimize performance

Get self-service recommendations on data partitioning and serialization.

Tune CPU and memory reservations based on actual consumption

Get self-service recommendations on container sizes and heap reservations.

Change queue selection or launch time based on cluster activity

Identify the best queue and launch time for applications based on current workloads.

Key Performance Indicators

  • Improves application efficiency and performance.
  • Increases productivity.

Rubicon Project Improves Performance and
Streamlines Automated Advertising Solution

Pepperdata Platform Spotlight gave Rubicon Project, a leading technology company that automates the process of buying and selling of advertising for advertisers, publishers, and ad agencies, the granular visibility they needed to quickly pinpoint, troubleshoot, and resolve problems in their cluster.

I develop a lot of complex Spark code to perform ETL on Hadoop clusters. In these complex, large-scale systems, you must be able to understand where the performance bottlenecks are. Pepperdata Application Spotlight gives developers detailed time-series performance data for things like CPU, JVM memory, and I/O usage overlaid against Spark job stages. I’m excited about the direction Pepperdata is moving — letting developers quickly see problems in time-series views and tie them back to their actual Spark application code will be a very useful tool for developers working on production Spark applications.- Software Engineer at Stripe and Pepperdata Technology Advisory Board Member
Chartboost is the world’s largest mobile games-only advertising platform, reaching one billion active players around the world every month. Chartboost utilizes Apache Spark on large Amazon EC2 Hadoop clusters for machine learning and ETL workflows. Understanding Spark application performance in these complex environments is always a challenge. As a current user of Pepperdata Platform Spotlight, it has been great to work with Pepperdata on the development of the Application Spotlight self-service portal software. It will give us a comprehensive insight into Spark jobs.- Manager of Data Engineering at Chartboost

See firsthand how Pepperdata solutions bring APM to every phase of the big data DevOps cycle with solutions for monitoring, tuning, troubleshooting, and improving collaboration between teams.

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