Pay Down Technical Debt In Your Data Pipeline With Great Expectations

Data pipelines are complicated and business critical pieces of technical infrastructure. Unfortunately they are also complex and difficult to test, leading to a significant amount of technical debt which contributes to slower iteration cycles. In this episode James Campbell describes how he helped create the Great Expectations framework to help you gain control and confidence in your data delivery workflows, the challenges of validating and monitoring the quality and accuracy of your data, and how you can use it in your own environments to improve your ability to move fast.

Announcements

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Your host is Tobias Macey and today I’m interviewing James Campbell about Great Expectations, the open source test framework for your data pipelines which helps you continually monitor and validate the integrity and quality of your data

Interview

Introduction
How did you get involved in the area of data management?
Can you start by explaining what Great Expecations is and the origin of the project?
- What has changed in the implementation and focus of Great Expectations since we last spoke on Podcast.__init__ 2 years ago?
Prior to your introduction of Great Expectations what was the state of the industry with regards to testing, monitoring, or validation of the health and quality of data and the platforms operating on them?
What are some of the types of checks and assertions that can be made about a pipeline using Great Expectations?
- What are some of the non-obvious use cases for Great Expectations?
What aspects of a data pipeline or the context that it operates in are unable to be tested or validated in a programmatic fashion?
Can you describe how Great Expectations is implemented?
For anyone interested in using Great Expectations, what is the workflow for incorporating it into their environments?
What are some of the test cases that are often overlooked which data engineers and pipeline operators should be considering?
Can you talk through some of the ways that Great Expectations can be extended?
What are some notable extensions or integrations of Great Expectations?
Beyond the testing and validation of data as it is being processed you have also included features that support documentation and collaboration of the data lifecycles. What are some of the ways that those features can benefit a team working with Great Expectations?
What are some of the most interesting/innovative/unexpected ways that you have seen Great Expectations used?
What are the limitations of Great Expectations?
What are some cases where Great Expectations would be the wrong choice?
What do you have planned for the future of Great Expectations?

Contact Info

LinkedIn
@jpcampbell42 on Twitter
jcampbell on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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