Databricks Cost Optimization

Factored helped a global enterprise cut Databricks cloud costs by 90% and reduce workflow times from 2 hours to 10 minutes with smart optimization.

Key Takeaways:

• Massive cost savings: Spark optimization and smart infrastructure cut cloud processing spend by 90%.

• Speed unlocked: Critical workflows now run in 10 minutes instead of 2 hours—boosting agility.

• Governance built in: Unity Catalog enabled scalable, secure, and well-governed data operations for future growth.

Cloud-based data platforms like Databricks Lakehouse are powerful, but without the right engineering practices, costs can quickly spiral and performance can lag.

Factored partnered with a major global enterprise to overhaul their Databricks environment

Reducing cloud processing costs by 90% and cutting workflow execution times from 2 hours to just 10 minutes.

The client faced skyrocketing costs and unreliable performance across their Databricks Lakehouse architecture. Key issues included:

• Lack of standardization and governance across Spark pipelines.

• High compute costs driven by inefficient workflows and poor resource management.

• Long SLA times for critical workflows, impacting downstream analytics and business decision-making.

They needed a way to optimize cloud spend without sacrificing scalability or operational excellence.

Architecture Assessment and Governance Setup

We conducted a deep technical audit, focusing on:

• Spark job performance and inefficiencies.

• Cost patterns across different workflows.

• Workflow criticality and service level requirements.

• Opportunities for cost-saving through smarter architecture choices.

Spark Performance Optimization

Our engineering team implemented best practices including:

• Predicate Pushdown to filter data at the source.

• Partition Pruning to limit scan sizes.

• Z-Ordering and Liquid Clustering to improve read/write efficiency.

• Optimized narrow and wide transformations to minimize shuffles across clusters.

Workflow Prioritization and Infrastructure Optimization

• Identified critical workflows requiring dedicated, reliable infrastructure.

• Shifted non-critical workloads to spot instances, achieving up to 70% cost savings without affecting business continuity.

• Created a structured framework to assess data request-to-return ratios for each workflow to minimize unnecessary compute spend.

Strategic Use of Databricks and Unity Catalog

Leveraged Unity Catalog for:

• Granular access controls.

• Advanced metadata management.

• Simplified integration and lineage tracking across all data assets.

• Ensured future scalability with strong governance embedded into the platform.

Implementation timeline: 8 months

• 90% reduction in cloud computing costs.

• Workflow SLA times reduced from 2 hours to 10 minutes.

• Increased platform scalability and reliability, supporting future data and ML initiatives.

• Stronger governance and metadata management through Databricks Unity Catalog.