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DTSTAMP:20261020T151709Z
DTSTART:20261104T204000Z
DTEND:20261104T211000Z
SUMMARY:Benchmarking Streaming Ingestion Engines for Apache Iceberg
LOCATION:Innovation Theater
DESCRIPTION:Daniel Liu\n\nApache Iceberg is becoming the lakehouse's shared
  storage layer. Every major warehouse reads it\, and a wave of new engines
  promise to stream Kafka into it. Once query engines share tables\, the in
 gestion engine becomes a separate choice. It determines whether tables sta
 y fresh\, data stays correct\, and files stay fast to query\, goals that p
 ull against each other. Warehouse benchmarks compare platforms with their 
 own ingest and storage\, leaving these engines without a common yardstick 
 others can rerun and check. We introduce ingest-bench\, built on three pri
 nciples. Reproducible: every engine replays the same fixed workload under 
 the same rules. Verifiable: the harness checks output tables directly\, no
 t engine metrics\, and publishes the evidence. Extensible: new engines\, w
 orkloads and test types plug in under the same scoring. We'll cover the de
 sign\, what it showed about Apache Flink\, Apache Spark and Eon Stream\, o
 ur own engine\, and how to apply it to your pipelines. By attending this s
 ession\, your contact information may be shared with the sponsor for relev
 ant follow up for this event only.
URL:https://sf26.rmoff.net/s/benchmarking-streaming-ingestion-engines-for-a
 pache-iceberg-223
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