uilding a lakehouse means continuously landing data in Apache Iceberg: Kafka events into bronze, OLTP increments, CDC into silver. The usual stack for that is Airflow + Spark plus a pile of glue code. For the common path “read → lightly reshape → write to Iceberg,” that stack is often overkill.
DataFlow Operator is a Kubernetes operator that declares ingest as a CRD: source → transformations → sink. It runs the processor, handles restarts and checkpoints, and—for batch jobs—can kick off post-load steps such as Spark on Iceberg tables.
Below is a short refresher on ETL, then three practical ways to land data in a lakehouse: streaming Extract, minimal streaming ETL, and batch ELT with Spark after the load.
What ETL is (and ELT / streaming next to it)
ETL means Extract, Transform, Load:
