The True Cost of Apache Spark™: How Enterprises Slash Spend by 50% or More

Spark has become the default platform to run ETL pipelines, yet most organizations dramatically overspend on it. Studies of production environments show that 30–70% of Spark compute is wasted which translates to millions of dollars in hidden costs. Default scaling mechanisms make the problem worse, leading to inflated bills and jobs that still finish too slowly.

In this webinar, you will learn:

• Why Spark pipelines are often far less efficient than most teams realize.
  
• How companies across industries have achieved 50–60% cost savings, reduced annual spend by hundreds of thousands of dollars, and delivered 10× faster performance.
  
• Proven strategies to analyze workloads, identify inefficiencies, and apply both DIY solutions like the free Cost Analyzer for Apache Spark and commercial solutions such as the Quanton engine to achieve 2–3× better price-performance.
  

Real-world examples:

• A global telecom provider achieved 10x faster Spark performance vs. Databricks, with real-time ingest latencies under 15 seconds.
  
• A workspace collaboration app processing 1PB/day cut Spark compute costs by 50% on AWS EMR.
  
• A large U.S. airline saved 3.8 million core-hours per year: a 60% reduction in Spark runtime costs.
  
• A public fintech company reduced Spark job runtimes by 45% and saved $800K annually.

If Spark is a significant line item in your budget, this session will show you how to reclaim wasted spend and reinvest it in innovation instead of infrastructure.