For an average scenario in Databricks where bronze tables are populated by Autoloaders and silver tables require some aggregations, the following best practices are widely recommended:

Bronze Layer Best Practices

• Land raw data in append-only Delta tables using Autoloader, with minimal transformation to preserve original data fidelity and auditability.​

• Store file metadata (e.g., source file name, load date) in the bronze tables to support lineage and troubleshooting.​

• Use partitioning (e.g., by date) to optimize query performance and lifecycle management.​

• Maintain the rawest data possible (including duplicates and errors), and avoid deleting original files until successful landing and validation is confirmed.​

Silver Layer Best Practices

• Read only from bronze; never load silver tables directly from sources, as this improves reliability and schema change handling.​

• Clean, validate, deduplicate, and normalize data in silver tables. Enforce schema, handle nulls, fix data types, and standardize formats.​

• Perform joins to combine related datasets or to slowly transform the data model (such as star or snowflake schemas). However, heavy aggregations are usually reserved for gold.​

• When aggregations are required in silver (e.g., for rapid downstream access or business logic), use incremental processes such as MERGE or appropriate upserts, and clearly separate raw/validated vs. aggregated tables.​

• Use streaming reads from bronze whenever possible, reserving batch reads for small, infrequently changing tables.​

• For daily snapshot data, store all versions in bronze and deduplicate in silver using logic like ROW_NUMBER() to keep only the latest record per business key.​

Additional General Recommendations

• Use Unity Catalog to govern table access and lineage across all layers, and register each table for easy discoverability and governance.​

• Implement data quality checks and monitoring to ensure accuracy and detect drifts, preferably at the silver layer or above.​

• Avoid irreversible transformations at the bronze stage so historic data can be reprocessed if downstream corrections are needed.​

• Design aggregations with consumption and scaling in mind, potentially pushing major aggregations to the gold layer for BI or ML workloads.​

By following these practices, a robust, scalable, and future-proof Databricks lakehouse pipeline can be maintained, supporting both operational reliability and downstream analytical needs.