Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.
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- Your host as usual is Tobias Macey and today I’m interviewing James Campbell and Abe Gong about Great Expectations, a tool for testing the data in your analytics pipelines
- How did you first get introduced to Python?
- What is Great Expectations and what was your motivation for starting it?
- What are some of the complexities associated with testing analytics pipelines?
- What types of tests can be executed to ensure data integrity and accuracy?
- What are some examples of the potential impact of pipeline debt?
- What is Great Expectations and how does it simplify the process of building and executing pipeline tests?
- What are some examples of the types of tests that can be built with Great Expectations?
- For someone getting started with Great Expectations what does the workflow look like?
- What was your reason for using Python for building it?
- How does the choice of language benefit or hinder the contexts in which Great Expectations can be used?
- What are some cases where Great Expectations would not be usable or useful?
- What have been some of the most challenging aspects of building and using Great Expectations?
- What are your hopes for Great Expectations going forward?
- Unplug and spend some time away from the computer
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA