Orchestrating Star Catalogs: Implementing a BigQuery Medallion Architecture
When handling massive astronomical datasets like star catalogs, a flat data structure simply won't scale. For the "Way-back-home" project, I implemented a BigQuery Medallion Architecture to ensure data quality, lineage, and high-performance querying. This kind of scalable infrastructure is essential for any digital transformation initiative.
From Bronze to Gold: The ETL Journey
The architecture follows the industry-standard three-layer approach. The Bronze layer ingests raw JSON and CSV data from cloud storage. The Silver layer cleanses and deduplicates these records using SQL-based transformations. Finally, the Gold layer serves optimized, aggregated star catalog data ready for analysis. This BigQuery Medallion Architecture ensures that complex queries remain cost-effective and lightning-fast.
Managing Orchestration with Cloud Composer
To keep the stars aligned, I utilized Cloud Composer (managed Apache Airflow) to orchestrate the ETL dependencies. Despite common API errors in cloud shell environments, the pipeline is designed with robust retry logic and error handling, ensuring that the star catalog remains synchronized across all layers of the medallion model.
