Map structured streaming jobs with checkpoints, watermarks, and exactly-once sinks
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Design Flink jobs with stateful operators, checkpoints, exactly-once sinks, and low-latency SLAs
Capture database changes in real time with connectors, schema control, and streaming transformations
Describe your ETL architecture and watch AI create a professional data pipeline diagram with proper connections and data flows
Common questions about ai spark streaming architecture generator
Use idempotent sinks (Delta/Iceberg) and checkpoints for offsets. Avoid side effects without deduplication.
Set watermarks and allowed lateness. Recompute windows and manage state TTLs.
Adjust micro-batch intervals, use autoscaling, and optimize partitions and state store.
Track batch duration, input rates, state size, and lag. Alert on checkpoint failures.
Use schema inference carefully; prefer explicit schemas and handle evolution via schema registry.
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