Databricks_ETL

A scalable ETL (Extract, Transform, Load) pipeline built on Databricks for processing and transforming data using Apache Spark.

Overview

This project implements an end-to-end ETL pipeline leveraging Databricks capabilities to:

• Extract data from various sources (cloud storage, databases, APIs)
• Transform data using PySpark and SQL
• Load processed data into Delta Lake tables
• Maintain data quality and governance

Project Structure

Databricks_ETL/
├── notebooks/          # Databricks notebooks for ETL processes
│   ├── bronze/        # Raw data ingestion
│   ├── silver/        # Data cleansing and transformation
│   └── gold/          # Business-ready aggregations
├── pipelines/         # Lakeflow Spark Declarative Pipelines
├── config/            # Configuration files
└── tests/             # Unit and integration tests

Prerequisites

• Databricks Workspace (AWS)
• Unity Catalog enabled
• Access to source data locations
• Appropriate IAM roles and permissions

Setup

1. Clone Repository

git clone <repository-url>
cd Databricks_ETL

2. Configure Data Sources

Update configuration files in config/ directory with your:

• Source data paths
• Target catalog and schema names
• Connection strings (if applicable)

3. Install Dependencies

%pip install -r requirements.txt

ETL Pipeline Architecture

Bronze Layer (Raw Data)

• Ingests raw data from source systems
• Minimal transformations
• Preserves original data structure
• Uses Auto Loader for incremental processing

Silver Layer (Cleaned Data)

• Data quality checks and validation
• Schema enforcement
• Deduplication
• Standardization and normalization

Gold Layer (Business Logic)

• Aggregations and business metrics
• Dimension and fact tables
• Optimized for analytics and reporting
• Ready for BI tools and dashboards

Usage

Running ETL Notebooks

1. Navigate to the notebooks/ directory
2. Execute notebooks in order: Bronze → Silver → Gold
3. Monitor job progress in the Databricks UI

Scheduling Jobs

# Schedule via Databricks Jobs UI or programmatically
# Set appropriate cluster configuration
# Configure alerts and notifications

Querying Data

-- Access processed data via Unity Catalog
SELECT * FROM main.etl_schema.gold_table
WHERE date = current_date()

Data Quality

• Schema validation at each layer
• Null checks and constraint enforcement
• Data profiling and statistics
• Automated quality reports

Monitoring & Logging

• Pipeline execution logs in Databricks
• Data lineage tracking via Unity Catalog
• Performance metrics and optimization
• Error handling and retry logic

Configuration

Key configuration parameters:

• source_path: Location of source data
• catalog_name: Unity Catalog catalog
• schema_name: Target schema
• checkpoint_location: Streaming checkpoint path
• data_quality_rules: Validation rules

Best Practices

• Use Delta Lake for ACID transactions
• Implement incremental processing
• Partition data appropriately
• Use liquid clustering for optimization
• Enable Change Data Feed when needed
• Follow Unity Catalog governance policies

Troubleshooting

Common Issues

• Permission Errors: Verify IAM roles and Unity Catalog grants
• Schema Mismatch: Check schema evolution settings
• Performance: Review partitioning and clustering strategy

Contributing

1. Create a feature branch
2. Make your changes
3. Test thoroughly
4. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or support, please contact the data engineering team.

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Built with ❤️ on Databricks