delta-live-tables
Delta Live Tables (DLT) pipeline patterns and examples for building declarative, self-healing data pipelines with automatic quality enforcement and lineage tracking.
Browse published AI skills maintained by vivekgana. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
12Repository stars across indexed skills
24Repository forks
0Average repository stars per indexed skill
2Repository stars may repeat across skills from the same repository.
Delta Live Tables (DLT) pipeline patterns and examples for building declarative, self-healing data pipelines with automatic quality enforcement and lineage tracking.
Modern deployment with Databricks Asset Bundles (DAB), supporting multi-environment configurations and CI/CD integration.
Bronze/Silver/Gold layer design patterns and templates for building scalable data lakehouse architectures. Includes incremental processing, data quality checks, and optimization strategies.
Unity Catalog governance patterns, permissions models, security best practices, and policy enforcement for enterprise data governance.
Data classification strategies, tagging frameworks, sensitivity levels, and automated classification patterns for Unity Catalog.
RBAC/ABAC implementation patterns, least privilege access, row-level security, column masking, and access review workflows.
Data product design patterns with contracts, SLAs, and governance for building self-serve data platforms using Data Mesh principles.
Delta Sharing configuration, monitoring, and recipient management for secure cross-organization data sharing.
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
Automated compliance checks, continuous monitoring, reporting frameworks for GDPR, HIPAA, SOC2, and CCPA compliance.
pytest fixtures and integration testing patterns for Spark applications, including DataFrame assertions and mock data generation.
GitHub Actions and CI/CD patterns for Databricks, including automated testing, deployment, and quality gates.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.