- Introduction
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About this Course
- Data Expectations Check Structure
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Adding and Referencing the Data Expectations Library and Modules
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Implement a Primary Key Check
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Exercise Summary
- Practice: Implement Data Expectations in Your Pipeline
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Column Expectations: “Is In”
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Group-by Expectations: “Is Unique”
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Schema Expectations
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Exercise Summary
- Conclusion
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Key Takeaways
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Next Steps
DATAENG 07 (Repositories): Configuring Data Expectations in Code Repositories
Learn how to configure and apply data expectations checks in pipeline builder and review them in the Data Health application.
In many cases, data health checks like the ones you applied in the previous tutorial will be sufficient to monitor your pipelines. A full monitoring and protection program should take advantage of the Data Expectations framework for greater granularity and control. In this brief tutorial, you’ll add encoded data checks to a few of your data transforms and view them in the Data Health application.
⚠️ Course prerequisites
- DATAENG 06: Maintaining Data Pipeline Health: If you have not completed this course, please do so now.
📖 Learning Objectives
- Understand when and how to apply data expectations checks.
💪 Foundry Skills
- Apply a Data Expectations check to an existing code repository.
- View expectations checks in the Data Health app.