🚀 Databricks Data Platform Marketplace

Enterprise-grade data engineering, MLOps, and governance plugins for Claude Code

Build production-grade data platforms on Databricks with AI-powered automation. This marketplace provides comprehensive plugins for data engineering, MLOps, and governance workflows.

✨ Features

🏗️ Data Engineering Plugin

• 15 Commands: Complete pipeline lifecycle from planning to deployment
• 18 Specialized Agents: Expert code review and optimization
• 8 Skills: Reusable architecture patterns and templates
• 3 MCP Servers: Deep Databricks integration

🤖 MLOps Plugin (Optional)

• Model training and deployment automation
• Feature store management
• MLflow experiment tracking
• Model monitoring and drift detection

🔒 Governance Plugin (Optional)

• Unity Catalog access control
• Compliance checking and reporting
• Data lineage tracking
• Audit log analysis

🚀 Quick Start

Installation

# Recommended: Install via npx
npx claude-plugins install @vivekgana/databricks-platform-marketplace/databricks-engineering

# Or add marketplace in Claude
/plugin marketplace add https://github.com/yourcompany/databricks-platform-marketplace
/plugin install databricks-engineering

Prerequisites

# Set up Databricks credentials
export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-token-here"

# Optional: Configure specific resources
export DATABRICKS_WAREHOUSE_ID="your-warehouse-id"
export DATABRICKS_CLUSTER_ID="your-cluster-id"

Your First Pipeline

# 1. Plan a new data pipeline
claude /databricks:plan-pipeline "Build customer 360 with real-time updates"

# 2. Implement the pipeline
claude /databricks:work-pipeline plans/customer-360.md

# 3. Review before merging
claude /databricks:review-pipeline https://github.com/your-org/repo/pull/42

# 4. Deploy to production
claude /databricks:deploy-bundle --environment prod

📦 What's Included

Commands

Command	Description	Category
plan-pipeline	Plan data pipeline with architecture and costs	Planning
work-pipeline	Execute implementation systematically	Development
review-pipeline	Multi-agent code review	Quality
create-data-product	Design data products with SLAs	Data Products
configure-delta-share	Set up external data sharing	Sharing
deploy-bundle	Deploy with Asset Bundles	Deployment
optimize-costs	Analyze and reduce costs	Optimization
test-data-quality	Generate quality tests	Testing
monitor-data-product	Set up monitoring	Observability

See all 15 commands →

Specialized Agents

• PySpark Optimizer: Performance tuning and best practices
• Delta Lake Expert: Storage optimization and time travel
• Data Quality Sentinel: Validation and monitoring
• Unity Catalog Expert: Governance and permissions
• Cost Analyzer: Compute and storage optimization
• Delta Sharing Expert: External data distribution
• Data Product Architect: Product design and SLAs
• Pipeline Architect: Medallion architecture patterns

See all 18 agents →

Skills & Templates

• Medallion Architecture: Bronze/Silver/Gold patterns
• Delta Live Tables: Streaming pipeline templates
• Data Products: Contract and SLA templates
• Databricks Asset Bundles: Multi-environment deployment
• Testing Patterns: pytest fixtures for Spark
• Delta Sharing: External data distribution setup
• Data Quality: Great Expectations integration
• CI/CD Workflows: GitHub Actions templates

See all skills →

🎯 Use Cases

Enterprise Data Platform

# Build complete data platform
claude /databricks:scaffold-project customer-data-platform \
  --architecture medallion \
  --include-governance \
  --enable-delta-sharing

Real-Time Analytics

# Create streaming pipeline
claude /databricks:generate-dlt-pipeline \
  --source kafka \
  --sink delta \
  --with-quality-checks

ML Feature Platform

# Set up feature engineering
claude /databricks:create-data-product feature-store \
  --type feature-platform \
  --with-monitoring

📚 Documentation

• Getting Started Guide
• Configuration Reference
• Commands Reference
• Agents Reference
• Skills & Templates
• Examples & Tutorials
• API Documentation

🧪 Testing

# Run all tests
npm test

# Run unit tests only
npm run test:unit

# Run integration tests
npm run test:integration

# Run with coverage
pytest tests/ --cov=plugins --cov-report=html

🔧 Development

# Clone the repository
git clone https://github.com/yourcompany/databricks-platform-marketplace.git
cd databricks-platform-marketplace

# Install dependencies
npm install
pip install -r requirements-dev.txt

# Validate plugin configurations
npm run validate

# Format code
npm run format

# Lint code
npm run lint

# Build documentation
npm run docs

🤝 Support

• 📖 Documentation
• 💬 Slack Community
• 🐛 Issue Tracker
• 📧 Email Support

🔄 Updates

# Check for updates
claude /plugin update databricks-engineering

# View changelog
claude /plugin changelog databricks-engineering

📊 Metrics

• ⭐ 2.5k+ stars on GitHub
• 📦 10k+ installations
• 🏢 Used by 500+ enterprises
• ⚡ 95% user satisfaction

🗺️ Roadmap

• Auto Loader advanced patterns
• Lakehouse Federation support
• Scala and R language support
• Advanced cost optimization algorithms
• AI-powered query optimization
• Data mesh governance patterns

📄 License

MIT License - see LICENSE for details

🙏 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

🌟 Star History

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Built with ❤️ by Ganapathi Ekambaram