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Big Data Processing
Unified Batch and Stream Processing
★ 4.5
Spark's Machine Learning Library
★ 4.5
pip install apache-beampip install pysparkpip install apache-beampip install pysparkPython 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 PySpark's `pyspark.ml` module to train machine learning models on datasets too large for scikit-learn. An MLlib Pipeline chains a `StringIndexer`, `VectorAssembler`, and `GBTClassifier` — fitting the pipeline on a distributed Spark DataFrame and saving the trained model to S3 for later scoring in a batch inference job.
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