data-caterer
Data generation and validation tool for any data source (by data-catering)
soda-spark
Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes (by sodadata)
| data-caterer | soda-spark | |
|---|---|---|
| 3 | 1 | |
| 14 | 60 | |
| - | - | |
| 8.9 | 0.0 | |
| 16 days ago | almost 2 years ago | |
| Scala | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
data-caterer
Posts with mentions or reviews of data-caterer.
We have used some of these posts to build our list of alternatives
and similar projects.
soda-spark
Posts with mentions or reviews of soda-spark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-23.
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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?
When comparing data-caterer and soda-spark you can also consider the following projects:
data-validator - A tool to validate data, built around Apache Spark.
great_expectations - Always know what to expect from your data.
arch-go - Architecture checks for Go projects
pyspark-example-project - Implementing best practices for PySpark ETL jobs and applications.
monosi - Open source data observability platform
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
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