databricks-expert

Unity Catalog provides centralized governance by organizing data into catalogs, schemas, and tables across workspaces. Set up a Unity Catalog environment by defining metastores, creating catalogs, configuring access controls, and assigning permissions to users and groups for fine-grained data access management.

MLflow on Databricks manages ML lifecycle through experiment tracking, model registry, and model serving. Log experiments, register models in the MLflow registry, apply security controls, and deploy models to serving endpoints while monitoring performance and enforcing governance policies.

Implement security through IAM policies, encryption at rest and in transit, access control via Unity Catalog, audit logging, and compliance frameworks. Apply role-based access, network isolation, and governance rules to enforce data protection across data engineering pipelines and model workflows.

Delta Lake on Databricks enables ACID transactions, schema enforcement, and time travel for reliable data pipelines. Combined with Unity Catalog governance, you gain centralized access control, audit trails, and compliance enforcement across your lakehouse architecture.

Use Databricks CLI for programmatic catalog management, workspace automation, and pipeline orchestration. Best practices include version-controlling deployment configurations, implementing least-privilege access, automating schema and table creation, and monitoring data lineage through Unity Catalog.

A well-architected Databricks platform applies governance, reliability, interoperability, and security pillars. Design with Unity Catalog for access control, Delta Lake for data reliability, MLflow for ML governance, and compliance frameworks to ensure consistent policies across data and ML workloads.