Post by Avantika Penumarty
Senior Data Engineer (Former @Meta) | Scaled Data Infrastructure for 1B+ Users | Empowering 20k+ Engineers to think in Systems, not Tools | AI & Data Tech Creator | Open to Senior IC Roles
Most Python tutorials are written for data scientists. Data engineers need something different. You don't need ML. You need pipelines that don't break at 3am. I built this cheatsheet around the 8 things I reach for on every single project: 1. File I/O that scales (parquet beats CSV every time) 2. DataFrame ops that actually run fast 3. DB connections with connection pooling 4. Chunked reads for files larger than your RAM 5. Schema validation before bad data wrecks downstream 6. Composable ETL with retry logic 7. Performance patterns (category dtype alone saved me hours) 8. The gotchas nobody puts in the docs Some of this seems obvious until it isn't. Then it's 2am and your pipeline is OOMing in prod. Save this for that night. ā»ļø Repost to help a data engineer. #DataEngineering #Python #ETL #Pandas #DataPipelines #SoftwareEngineering
