Building Incremental Data Pipelines for Out-of-Order Scientific Data

- This article discusses a technical challenge in data engineering: building incremental (rather than batch) data pipelines that can handle "out-of-order" data, meaning records that arrive after earlier data has already been processed. - The context is scientific data pipelines, where sensors or instruments produce time-stamped data, but the data often arrives late, in chunks, or in the wrong sequence. Traditional batch processing struggles with this. - The author contrasts a simple "stateless" approach (re-ingest everything every time) with a more efficient "stateful" approach (record watermarks or progress points so only new or late data is processed). - Key concepts covered include: idempotency (re-running a pipeline produces the same result), watermarks (a threshold marking how much data has been safely processed), and handling "late-arriving" or "out-of-window" data that falls outside the normal processing window. - The piece assumes familiarity with data pipeline concepts (like batches, incremental processing, and event time vs. processing time) and is written for engineers working on real-time or near-real-time data systems.