GitHub Actions and CI/CD patterns for Databricks, including automated testing, deployment, and quality gates.
Data product design patterns with contracts, SLAs, and governance for building self-serve data platforms using Data Mesh principles.
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
Modern deployment with Databricks Asset Bundles (DAB), supporting multi-environment configurations and CI/CD integration.
Delta Live Tables (DLT) pipeline patterns and examples for building declarative, self-healing data pipelines with automatic quality enforcement and lineage tracking.
Delta Sharing configuration, monitoring, and recipient management for secure cross-organization data sharing.
pytest fixtures and integration testing patterns for Spark applications, including DataFrame assertions and mock data generation.
Data classification strategies, tagging frameworks, sensitivity levels, and automated classification patterns for Unity Catalog.