AlexIoannides/pyspark-example-project resource

Health v2 · maintenance only

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2119 stars · 808 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A reference example project demonstrating best practices for structuring PySpark ETL jobs and applications. It shows how to organize code for testability, pass configuration, handle dependencies, and write meaningful unit tests for Spark ETL pipelines.

Use cases

• structure a pyspark etl job for testability
• learn best practices for spark etl projects
• unit test spark transformations with test data
• pass config parameters to a pyspark job
• package dependencies for spark-submit
• template for starting a pyspark data engineering project

When to choose

• starting a new PySpark ETL project and wanting a proven structure
• learning how to test Spark transformations in isolation
• looking for conventions for configs, dependencies, and packaging for Spark clusters

When to avoid

• you need a production ETL framework or orchestrator rather than a reference example
• you use Scala/Java Spark instead of Python
• you need streaming or complex pipeline orchestration features

Facets

learning-resource · maturity maintenance

etl testing developer-tools big-data tutorials python jvm cross-platform pyspark apache-spark etl-pipeline best-practices example-project data-science data-engineering

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