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Big Data Processing
Unified Batch and Stream Processing
★ 4.5
Scalable Machine Learning Platform
★ 4.4
pip install apache-beampip install h2opip install apache-beampip install h2oPython data engineers use Apache Beam to write portable data pipelines that run locally for testing and deploy to Google Dataflow or Flink in production without code changes. The Python SDK's `PCollection` API chains transforms — `ReadFromBigQuery | Map(transform_fn) | WriteToBigQuery` — enabling the same pipeline logic to handle both batch backfills and live streaming.
Python data engineers use the `h2o` Python package to run distributed AutoML experiments on large training datasets that exceed single-machine memory. Engineers call `h2o.automl.H2OAutoML().train()` pointing at an H2OFrame backed by a cluster, let H2O evaluate dozens of algorithms, then export the best model as a MOJO for deployment in a Python scoring service.
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