PySpark-Boilerplate
soda-spark
| PySpark-Boilerplate | soda-spark | |
|---|---|---|
| 1 | 1 | |
| 390 | 60 | |
| - | - | |
| 2.5 | 0.0 | |
| 3 months ago | almost 2 years ago | |
| Python | Python | |
| - | Apache License 2.0 |
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PySpark-Boilerplate
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?
ibis - the portable Python dataframe library
great_expectations - Always know what to expect from your data.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
pyspark-example-project - Implementing best practices for PySpark ETL jobs and applications.
cookiecutter-django - Cookiecutter Django is a framework for jumpstarting production-ready Django projects quickly.
monosi - Open source data observability platform
etl-markup-toolkit - ETL Markup Toolkit is a spark-native tool for expressing ETL transformations as configuration
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
Traffic-Data-Analysis-with-Apache-Spark-Based-on-Mobile-Robot-Data - Mobile robot data were analyzed with Apache-Spark to extract five different statistical result such as travel time, waiting time, average speed, occupancy and density were produced.
TypedPyspark - Type-annotate your spark dataframes and validate them
tdigest - t-Digest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark
data-caterer - Data generation and validation tool for any data source