data-pipeline

To build an ETL pipeline into PostgreSQL, you extract data from APIs, transform it with Pydantic v2 validation, and load it using SQL ON CONFLICT for idempotent upserts. This ensures reliable batch processing without duplicating records during pipeline reruns.

Pydantic v2 validation in a batch ETL pipeline works by enforcing strict schema rules on transformed records before loading. This guarantees that only structurally valid data reaches PostgreSQL, preventing corrupt data from entering your database.

Yes, the data pipeline supports API pagination and checkpointing for large-scale ingestion. It streams paginated API responses and saves progress, allowing scalable data ingestion to resume safely from the last checkpoint if interrupted.

Yes, you can run batch ETL processing in a Django environment using a built-in management command. This allows you to execute, monitor, and schedule data pipelines directly within your existing Django application infrastructure.

The best way to handle batch sizing when loading API data into PostgreSQL is to process records in configured batch sizes. Batching controls memory usage during data ingestion and optimizes the upsert loading performance for large datasets.

You use idempotent upserts for API data ingestion to ensure data consistency when rerunning failed pipelines. By utilizing SQL ON CONFLICT handling, the pipeline updates existing records or inserts new ones without creating duplicates.