# 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.