Data Ingestion Builder

소개

Streamline the process of adding new data sources to your analytics pipeline with a structured, six-step workflow designed for reliability and consistency. This tool guides you through every stage of provider development, from initial data source research and registry design to implementing storage-abstracted loaders and automated primary key validation. By enforcing principles like metadata sidecars and uniform Parquet output, it ensures that every new data source—whether from an API, database, or flat file—integrates seamlessly with both local and cloud-based storage environments while maintaining high data quality standards through automated sampling and testing.

주요 기능

• Automates primary key validation and data quality testing for new providers
• Includes standardized templates for registries, loaders, and sample data generation
• Provides storage abstraction for uniform Parquet output with metadata sidecars
• Supports both local paths and Google Cloud Storage (GCS) through consistent helpers
• Implements the Registry Pattern for central mapping of datasets to loader functions
• 0 GitHub stars

사용 사례

• Standardizing existing data loaders to follow a metadata-first architecture
• Expanding data pipeline coverage by adding support for new file formats or databases
• Integrating new fantasy football data sources like ESPN, Sleeper, or PFF APIs