How to design scalable data pipelines that work at scale

Pipelines that work for 10 runs often crumble at 1000. What used to be a clean setup turns into: ◾fragile DAGs that need manual restarts ◾slow orchestration ◾unclear ownership when things fail Scaling data operations isn't about ‘more compute’. It's all about a better design that allows observability, parallelization, and platform automation that actually scale. Will your pipelines still hold up when data volume or orchestration frequency increases? Do not wonder, take the test ➡️ https://lnkd.in/d9eSkTDP #DataEngineering #DataPlatform #DataOps #InfrastructureAsCode