Bronze, Silver & Gold Pipeline Design: 7 Databricks Best Practices to Follow

4. Use Incremental Processing Where Appropriate

Processing an entire dataset every time a pipeline runs can increase compute consumption and processing time.

Where the workload supports it, incremental processing can focus on newly arrived or changed records rather than repeatedly processing historical data.

The appropriate approach depends on source-system behavior, data volume, transformation complexity, and latency requirements.

This is among the Databricks Best Practices worth evaluating when designing scalable pipelines.

5. Build for Schema Evolution

Source systems change. New columns may appear, existing fields may change, and upstream applications may introduce unexpected data structures.

A robust pipeline should account for controlled schema evolution rather than assuming source schemas will remain unchanged indefinitely.

Schema validation, monitoring, and clearly defined compatibility rules can help prevent upstream changes from breaking downstream workloads.

For critical pipelines, testing schema changes before production deployment can further reduce operational risk.

6. Add Observability Across Every Layer

A pipeline can successfully complete while still producing incorrect data.