# AlexIoannides/pyspark-example-project

Implementing best practices for PySpark ETL jobs and applications.

Repository: https://github.com/AlexIoannides/pyspark-example-project
Canonical: https://ross.abutalabs.com/products/pyspark-example-project
Language: Python
License Family: other
Topics: pyspark, etl-job, python, data-engineering, spark, data-science, etl, etl-pipeline
Archived: true
Last push: 2023-01-01T22:08:44+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3170, "days_push": 1340, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2119, forks 808 (observed 2026-08-28T04:06:16.458916+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: etl, testing, developer-tools
- domain: big-data, tutorials
- platform: python, jvm, cross-platform
- tags: pyspark, apache-spark, etl-pipeline, best-practices, example-project, data-science, data-engineering

## Member repositories
- AlexIoannides/pyspark-example-project (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.458916+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:01.494000+00:00, confidence not recorded.
  - readme: https://github.com/AlexIoannides/pyspark-example-project (fetched 2026-08-28T04:06:16.458916+00:00, sha 384dde179c1e)
- Data as of 2026-08-30T08:39:29.467469+00:00.