Application Recommendations

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

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 Big Data Performance and
Streamlines Automated Advertising Solution

Rubicon Project automates the process of buying and selling of advertising for advertisers, publishers, and ad agencies. Platform Spotlight gave Rubicon Project the granular visibility they needed to quickly pinpoint, troubleshoot, and resolve problems in their multi-tenancy Hadoop 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

Request a trial to see firsthand how Pepperdata big data solutions can help you achieve big data performance success. Pepperdata’s proven APM solutions provide a 360° degree view of both your platform and applications, with realtime tuning, recommendations, and alerting. See and understand how Pepperdata big data performance solutions helps you to quickly pinpoint and resolve big data performance bottlenecks. See for yourself why Pepperdata’s big data APM solutions are used to manage performance on over 30K Hadoop production clusters.

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