All stacks · Compute & processing
Portable batch and streaming pipelines — Dataflow, Flink, Spark runners.
Apache BeamWrite-once Beam is real. The Dataflow bill is vCPU-hours plus Shuffle plus Streaming Engine. Streaming jobs keep a worker all night. Spark is cheaper if the team already writes Spark.
GCPA production comparison of Dataflow and Dataproc on GCP: what Beam buys you, how the two billing models differ, and the team characteristics that decide the choice.
Apache Beam is the portable programming model: one pipeline, runners for Dataflow, Flink, Spark, and others. Google Cloud Dataflow is the managed runner most people mean when they say Beam.
The value is not “write once, run anywhere” as a slogan — it is event-time windows and watermarks that survive a runner change. The cost is a smaller hiring pool than Spark.
No. Dataflow is the common managed runner, but Flink and Spark runners exist. If you are all-in on Databricks, Beam is usually the wrong default. If you are on GCP and already think in Dataflow, Beam is the API.
Spark if your team already runs Spark and the job is batch. Beam if you want portable streaming semantics and Dataflow ops. Do not run both as a culture; pick one programming model per platform.
Streaming jobs that never scale to zero, hot keys, and shuffling the world every window. Worker hours are the meter. Profile the job graph before you add another transform “to be safe.”
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