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Architecture choices define your data system. Bad choices make changes hard. Good choices keep your options open.

Define your problem first. Set clear goals for your ETL and stream processing. This stops over-engineering.

Start with a simple build. Get a working system end-to-end. Add features later.

Test every part. Cover edge cases. Monitor your performance. Set alerts for errors.

Do not build for scale you do not have. Refactor as you learn. Track technical debt. Fix it before it slows you down.

Follow these rules:

Action plan:

Source: https://dev.to/therizwansaleem/pipeline-architecture-patterns-data-processing-etl-and-stream-processing-at-scale-4dkp