Building a weather data warehouse part I: Loading a trillion rows of weather data into TimescaleDB

The post discusses the process of building a weather data warehouse using PostgreSQL and TimescaleDB. It explores the need for historical weather data, the ERA5 climate reanalysis product, and the challenges of loading large amounts of data into a database. The post compares different insertion methods, including single-row inserts, multi-valued inserts, and the copy statement. It also evaluates the performance of external tools like pg_bulkload and timescaledb-parallel-copy. The conclusion suggests using psycopg3 to directly copy data into a hypertable or using timescaledb-parallel-copy if CSV files are available. The post provides detailed benchmarks and considerations for achieving optimal insert rates and loading times.