πππππ«π¦ππ«π€π’π§π & ππ‘πππ€π©π¨π’π§ππ’π§π
Published 2026-08-25 in Data Engineering
πππππ«π¦ππ«π€π’π§π β Watermarking helps handle late-arriving data in event-time processing. β It defines how long Spark should wait for delayed events before considering them too late to process. This also helps clean up old state and prevents unnecessary memory usage. ππ‘πππ€π©π¨π’π§ππ’π§π β Checkpointing stores the state and progress of a streaming query. β If a job stops due to a failure or restart, Spark resumes processing from the last checkpoint instead of starting from the beginning, enabling fault tolerance and exactly-once processing. A simple way to remember: βͺοΈ Watermarking β Decides when to stop waiting for late data. βͺοΈ Checkpointing β Decides where to resume after a failure. Both features are essential for building reliable, scalable, and fault-tolerant streaming pipelines in Databricks.
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