Data Observability for Analytics and Machine Learning (ML) Teams

This resource provides principles, practices, and examples for ensuring high quality data flows.


Data observability is a set of principles that can be implemented in tools to ensure data is accurate, up-to-date, and complete. This principle is increasingly necessary, especially as traditional approaches to software monitoring fall short for high-volume, high-variety data. Unit tests, which assess small pieces of code for performance on discrete, deterministic tasks, get overwhelmed by the variety of acceptable shapes and values that real-world data can take.

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