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Starlake: Open Source Data Integration & ETL Platform

Starlake is an enterprise-grade data pipeline platform that transforms how organizations handle data integration. Using a declarative approach with YAML and SQL, it eliminates complex coding while ensuring robust data governance and quality.

Revolutionize Your Data Workflows​

Transform your data engineering with Starlake's innovative approach to pipeline automation. Our platform reduces development time by 80% while ensuring enterprise-grade reliability and scalability.

Supported Data Sources​

Starlake supports a comprehensive range of data sources:

File Formats​

  • Structured: CSV, TSV, PSV (with multi-char separators)
  • Semi-structured: JSON, XML, YAML
  • Binary: Parquet, Avro, ORC
  • Compressed: ZIP, GZIP, BZIP2
  • Fixed-width position files
  • Excel spreadsheets

Cloud Storage​

  • Amazon S3
  • Google Cloud Storage
  • Azure Blob Storage
  • Local filesystem

Databases & Warehouses​

  • BigQuery
  • Snowflake
  • Redshift
  • Databricks
  • PostgreSQL
  • MySQL
  • Oracle
  • SQL Server
  • DuckDB

Streaming & Events​

  • Apache Kafka
  • Amazon Kinesis
  • Google Pub/Sub
  • Azure Event Hubs

Enterprise Features​

Cloud-Native Integration​

  • Native support for AWS, GCP, and Azure
  • Cloud-agnostic deployment options
  • Serverless execution capabilities
  • Multi-cloud data synchronization

CI/CD Integration​

  • Git-based version control
  • Automated testing with DuckDB
  • Pipeline validation workflows
  • Infrastructure as Code support
  • Integration with:
    • GitHub Actions
    • GitLab CI
    • Azure DevOps
    • Jenkins

Security & Governance​

  • Row-level security
  • Column-level encryption
  • Data masking and anonymization
  • Audit logging and compliance
  • GDPR and CCPA support

Code Less, Deliver More​

Break free from traditional ETL complexity. Starlake's declarative approach means you can:

  • Build data pipelines using simple YAML configurations
  • Transform data with familiar SQL statements
  • Automate workflow orchestration without custom scripts
  • Deploy production-ready pipelines in minutes, not months

Complete Data Lifecycle Management​

Take control of your entire data journey:

  • Automated data extraction from 20+ source types
  • Real-time data quality monitoring and validation
  • End-to-end data lineage tracking
  • Comprehensive audit trails and version control
  • Automated schema evolution and compatibility checks

Enterprise Data Governance​

Ensure data quality and compliance at scale:

  • Real-time data quality monitoring with customizable rules
  • Automated schema enforcement and validation
  • Built-in privacy controls and security features
  • SLA monitoring and alerting
  • Comprehensive audit logging and reporting

Flexible Deployment Options​

Deploy Starlake your way:

  • Self-hosted deployment with Docker
  • Native cloud integration with AWS, GCP, and Azure
  • Local development environment for rapid testing
  • Seamless production scaling

Comprehensive Pipeline Solutions​

No-Code Data Ingestion​

Simplify data ingestion with built-in connectors:

  • Support for CSV, JSON, XML, and fixed-width formats
  • Native integration with major data warehouses
  • Real-time streaming support via Kafka
  • Automated schema inference and validation
  • Built-in error handling and recovery

Low-Code Transformations​

Transform data efficiently using familiar tools:

  • Write transformations in standard SQL
  • Define business rules in YAML
  • Automatic handling of incremental updates
  • Built-in support for complex data types
  • Version control and change management

Automated Workflow Orchestration​

Let Starlake manage your pipeline complexity:

  • Automatic dependency management
  • Native integration with Airflow and Dagster
  • Visual DAG generation and monitoring
  • Parallel execution optimization
  • Built-in error handling and recovery

How Starlake Works​

1. Extract​

Choose from two powerful extraction modes:

  • Native Mode: Generate optimized database-specific scripts
  • JDBC Mode: Achieve 1M+ records/second with parallel extraction
  • Support for incremental and full loads
  • Automated schema evolution handling

2. Load​

Transform data ingestion into a declarative process:

  • Zero-code data loading with YAML configurations
  • Automated data quality validation
  • Built-in privacy and security controls
  • Support for all major data warehouses
  • Real-time monitoring and alerting

3. Transform​

Simplify transformations with SQL and YAML:

  • Write standard SQL SELECT statements
  • Automatic optimization for your target platform
  • Built-in support for incremental processing
  • Comprehensive data quality validation
  • Version control and rollback capabilities

4. Orchestrate​

Automate your entire data pipeline:

  • Intelligent dependency management
  • Integration with enterprise schedulers
  • Visual pipeline monitoring
  • Efficient parallel execution
  • Automated error handling and recovery

Getting Started​

Begin your journey to modern data pipeline automation:

  1. Define your data sources using simple YAML
  2. Write transformations in familiar SQL
  3. Let Starlake handle the orchestration

Visit our Quick Start Guide to see how Starlake can transform your data engineering workflow.