Problem Statement

The organization observed growing Databricks spend alongside slower-than-expected job execution. Analytics pipelines took longer to complete, delaying downstream dashboards and customer insights. Engineering teams faced mounting pressure to improve performance while keeping cloud costs under control.

The objective was to increase analytics throughput, stabilize performance, and reduce Databricks costs without disrupting customer operations.

Key Challenges

• Inefficient ETL and Feature Engineering Jobs

  Several recurring pipelines processed large datasets inefficiently, driving longer runtimes and higher compute usage.

• Overlapping and Poorly Orchestrated Jobs

  Jobs frequently ran in parallel without coordination, increasing concurrency and forcing clusters to scale beyond actual needs.

• Underutilized Compute Resources

  Clusters were often over-provisioned for peak loads but underutilized for most execution windows.

• Limited Visibility into Runtime and Cost Drivers

  Teams lacked clear insight into which pipelines and execution patterns were responsible for rising Databricks usage.

Solution Implemented

• Workload and Runtime Analysis : Credencys analyzed job execution timelines, resource utilization, and concurrency patterns across the Customer 360 pipelines.

• Spark and Pipeline Optimization : ETL and feature engineering jobs were tuned to reduce execution time through improved partitioning, optimized joins, and better caching strategies.

• Job Orchestration Refinement : Job schedules were restructured to reduce overlap, flatten peak concurrency, and improve overall cluster efficiency.

• Cluster Right-Sizing and Scaling Adjustments : Clusters were resized and scaling thresholds refined to better match workload demand throughout the day.

• Workload-Level Visibility Enablement : Teams gained clearer insight into pipeline performance and resource consumption, enabling proactive optimization decisions.

Business Impact

• 28% Improvement in Analytics Throughput

  Optimized pipelines processed data faster, enabling more frequent and timely customer insights.

• Lower and More Predictable Databricks Costs

  Reduced runtime and improved orchestration helped control compute consumption as usage scaled.

• Improved SLA Adherence for Customer Deliverables

  More stable execution timelines reduced delays in dashboards and analytics outputs.

• Operational Efficiency for Engineering Teams

  Teams spent less time troubleshooting slow jobs and more time improving platform capabilities.