Best practices in Databricks

Replace traditional partitioning and Z-Ordering with Liquid Clustering. It automatically adjusts data layout based on query patterns, solving the small-files problem and improving query performance without manual tuning.

Use Serverless SQL Warehouses and Serverless Jobs to eliminate idle time and infrastructure management. Serverless compute scales instantly and charges only for the seconds used.

For Unity Catalog managed tables, enable Predictive Optimization to automatically run maintenance operations like OPTIMIZE and VACUUM at the optimal time.

Use Databricks Asset Bundles for Infrastructure as Code (IaC) and CI/CD. DABs let you define jobs, pipelines, and infrastructure in YAML and deploy them consistently across Dev, Staging, and Prod.

Leverage Lakeflow (Delta Live Tables) to build reliable ETL pipelines. Define data transformations declaratively and let Databricks handle orchestration, error handling, and auto-scaling.

Integrate Databricks Repos with your Git provider (GitHub, Azure DevOps) to enable branch-based development, code reviews, and version history for notebooks and code.

Centralize access control, auditing, and data discovery with Unity Catalog. It provides a unified governance layer for all data and AI assets (tables, files, models) across workspaces.

Implement ABAC to create scalable access policies based on tags (e.g., PII, Confidential) rather than managing permissions for individual users and tables.

Use Clean Rooms for secure collaboration with external partners. Share data and run joint analyses without exposing the underlying raw data or moving it out of your environment.

Build and deploy production-grade GenAI agents using the Mosaic AI Agent Framework. It provides tools for evaluation, tracing, and deployment of LLM applications.

Use Mosaic AI Vector Search to build RAG (Retrieval Augmented Generation) applications. It automatically syncs your Delta tables to a vector index for fast semantic search.