pyspark-example-project
soda-spark
| pyspark-example-project | soda-spark | |
|---|---|---|
| 1 | 1 | |
| 1,370 | 60 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 2 years ago | |
| Python | Python | |
| - | Apache License 2.0 |
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pyspark-example-project
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Learning Pyspark for a new role
https://github.com/AlexIoannides/pyspark-example-project You can use this as an example to organize your project. I have referred to this in the past.
soda-spark
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How do you test your pipelines?
Since you already have Spark setup, perhaps it would be easier to build a DataFrames by loading data from different tables and validate it in one go ? You can give soda-spark a try (disclosure: I'm one of the developers), using which you can specify your checks using YAML declaratively and run the validations in spark jobs.
What are some alternatives?
Apache-Spark-Guide - Apache Spark Guide
great_expectations - Always know what to expect from your data.
patterns-devkit - Data pipelines from re-usable components
monosi - Open source data observability platform
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
TypedPyspark - Type-annotate your spark dataframes and validate them
dados-censup - Automação da ingestão de dados disponibilizados pelo INEP referente ao censo superior da educacão brasileira.
data-caterer - Data generation and validation tool for any data source