Big Bad World of Postgres Dev Environments
summary
Three concrete workflows and the exact tools to pick between them: Postgres.app for fast local dev, Docker images (custom images to include PostGIS or pgvector), or cloud forks/replicas and template databases built from schema‑only dumps. Use PG stat statements and EXPLAIN for query visibility, pg_dump or Django dumpdata for data, and the Postgres anonymizer (Dollybo) to mask production data into templates; people report this masking-plus-template approach reliably gives safe, realistic dev copies.
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chapters (10)
- 0:00Postgres dev environments intro
- 1:05Why dev environments matter
- 4:32Local Postgres setups trade-offs
- 6:39Docker for Postgres dev
- 7:20Postgres extensions in Docker
- 8:44Cloud Postgres dev environments
- 10:12Template databases for cloning
- 11:15Realistic test data generation
- 12:42Anonymizing dev data
- 15:10Summary and Q&A
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