Describe your sources and the AI draws the full ingestion layer — batch, CDC, streaming, and API pulls, with landing zones and failure paths
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Describe your ETL architecture and watch AI create a professional data pipeline diagram with proper connections and data flows
Capture and stream database changes reliably to lakes, warehouses, and caches
Design Kafka deployments showing topics, producers, consumers, Kafka Streams, and Kafka Connect
Common questions about ai data ingestion architecture generator
Yes — dead-letter queues, quarantine buckets, and replay paths render as first-class parts of the flow. Ingestion diagrams that only show the happy path are the ones that lie during incidents, so the generator asks about failure handling when the prompt implies it.
Describe both and they render as parallel paths into the same landing zone, with the batch/streaming distinction visible in the edge styling — the lambda-vs-kappa conversation is much easier with the actual paths drawn.
Fivetran, Airbyte, dlt, Debezium, Kafka Connect, Kinesis, Pub/Sub, Event Hubs, NiFi, and plain COPY-from-storage patterns — plus the schedulers that trigger them. Mixed estates are the norm and render honestly.
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