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.
Tobias Macey
Posted on • Originally published at dataengineeringpodcast.com
Pay Down Technical Debt In Your Data Pipeline With Great Expectations - Episode 117
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