Uncovering Hidden Bottlenecks: How to Cut Apache Spark™ Compute Costs by 50%+

Most Spark jobs run far below their potential, with 30–70% of compute wasted in typical production workloads. Common culprits include inefficient shuffles, skewed joins, over-provisioned executors, and Spark’s default autoscaler, which scales up too slowly and almost never scales down efficiently. The result is ballooning cloud bills and unpredictable runtimes.

This session takes a deep dive into the mechanics of improving Spark efficiency by 2-3x:

• Identifying stage-level bottlenecks such as misaligned partitioning, wide dependencies, and serialization overheads that consume the bulk of resources.
  
  
• Fixing autoscaling gaps that prevent executor allocation algorithms from handling steady-state or bursty workloads effectively.
  
  
• Analyzing workload fingerprints that reveal how extract-heavy, transform-intensive, and load-bound jobs exhibit distinct waste patterns.
  
  
• Applying optimization levers including systematic workload analysis, DIY approaches with the free Spark Analyzer, and commercial solutions such as the Quanton engine that deliver 2–3× better price-performance.
  
  

Attendees will walk away with a clear framework to quantify Spark waste, pinpoint root causes at the stage level, and apply proven techniques to cut spend by 50% or more while accelerating runtimes.