Problem Statement

Despite stable workloads, the organization saw Databricks costs increase month over month. Engineering teams struggled to explain the spikes, and leadership lacked clarity on which jobs or clusters were driving spend. Performance issues further compounded the problem, with long-running jobs delaying downstream analytics and decision-making.

The organization needed to reduce Databricks costs without slowing analytics or disrupting active business users.

Key Challenges

• Escalating Databricks Cloud Spend

  Databricks compute costs were growing disproportionately compared to workload growth, creating budget overruns and internal scrutiny.

• Inefficient Spark Job Performance

  Several critical pipelines ran for hours due to suboptimal Spark configurations, inefficient joins, and poor data partitioning.

• Over-Provisioned and Always-On Clusters

  Clusters were sized for peak usage and left running even during idle periods, leading to unnecessary DBU and cloud infrastructure consumption.

• Limited Visibility into Cost Drivers

  Teams lacked workload-level insight into which jobs, clusters, or users were responsible for the majority of Databricks spend.

Solution Implemented

• Databricks Usage and Cost Assessment : Credencys conducted a deep analysis of cluster configurations, job execution patterns, and workload behavior to identify hidden cost drivers.

• Spark Code and Query Optimization : Critical Spark jobs were refactored to improve execution efficiency through optimized joins, partition strategies, and caching mechanisms.

• Cluster Right-Sizing and Policy Alignment : Cluster sizes and instance types were aligned with actual workload requirements, eliminating excess capacity across environments.

• Job Scheduling and Resource Optimization : Long-running and overlapping jobs were re-scheduled to reduce peak concurrency and unnecessary scaling.

• Idle Resource Cleanup : Unused clusters and orphaned workflows were identified and removed, stopping silent cost leakage across the Databricks environment.

Business Impact

• 90% Reduction in Databricks Cloud Costs

  Targeted optimization across compute, Spark workloads, and cluster usage dramatically reduced overall Databricks spend.

• Workflows Accelerated from Hours to Minutes

  Critical jobs that previously took nearly two hours were optimized to complete in as little as 10 minutes.

• Improved Platform Stability and Predictability

  Optimized workloads reduced contention and performance variability across shared Databricks environments.

• Greater Cost Transparency for Engineering Teams

  Teams gained clarity into how design and scheduling decisions impacted Databricks usage and cost.