What is great_expectations?
Great Expectations helps teams save time and promote analytic integrity by offering a unique approach to automated testing: pipeline tests. Pipeline tests are applied to data (instead of code) and at batch time (instead of compile or deploy time). Pipeline tests are like unit tests for datasets: they help you guard against upstream data changes and monitor data quality.
Software developers have long known that automated testing is essential for managing complex codebases. Great Expectations brings the same discipline, confidence, and acceleration to data science and engineering teams.
How do I get started?
It is super easy...
Just use pip install:
$ pip install great_expectations
Learn it live with us!
To make this project more accessible to all data practitioners, we are looking for user testers to help make the onboarding experience better!If you are new to Great Expectations, in return for your time we’d love to help you get up and running!Schedule a 1 hr session here!
You can also clone the repository, which includes examples of using great_expectations.
$ git clone https://github.com/great-expectations/great_expectations.git
$ pip install great_expectations/
Great Expectations users are enjoying their experience.
We dont' like to brag, but we don't mind letting the Great Expectations user base do it for us. Here are a few nice things folks have said about GE.
"Now that we're using Great Expectations we get notified ahead of time when data does not look right, giving us time to investigate and alert data users before they find out themselves"
Still want more?
Watch our walkthroughs
Read our core philoshophy
Read Down with Pipeline Debt! explains the core philosophy behind Great Expectations. Please give it a read, and clap, follow, and share while you're at it.
Read about our latest release!
We are super excited to announce the release of one of Great Expectations’ most requested and most anticipated features: Spark execution. Read about it!