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.
- 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.
Implementation timeline: 8 months
