Trust Rules to validate your data, AgileData Engineering Pattern #3 - Episode #69

Episode 69 of the Agile Data N’ Info podcast, hosted by AgileData, titled "Trust Rules to validate your data, AgileData Engineering Pattern #3 - Episode #69" was published on July 13, 2025 and runs 12 minutes.

July 13, 2025 ·12m · Agile Data N’ Info

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Summary

Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern of Trust Rules to validate data. The Trust Rules pattern provides automated data validation to ensure all incoming data is fit for purpose and trustworthy. It bakes in essential checks such as unique business keys and business effective dates, which run automatically upon data load or table refresh. Users can also define custom validation rules for specific columns. Results are collected, persisted, and surfaced via applications or alerts, with the system optimising validation for cost and speed through smart partitioning and clustered columns. An AgileData Engineering Pattern is a repeatable, proven approach for solving a common data engineering challenge in a simple, consistent, and scalable way, designed to reduce rework, speed up delivery, and embed quality by default. If you want a copy of the pattern template head over to: https://agiledata.substack.com/i/167964917/pattern-name Discover more AgileData Engineering Patterns over at https://agiledata.substack.com/s/agiledata-engineering-patterns If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn. Subscribe: Apple Podcast | Spotify | Google Podcast | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser | Deezer | Podcast Addict | Buy the Green Book now! Simply Magical Data Ways of Working

Episode Description

Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern of Trust Rules to validate data.

The Trust Rules pattern provides automated data validation to ensure all incoming data is fit for purpose and trustworthy. It bakes in essential checks such as unique business keys and business effective dates, which run automatically upon data load or table refresh. Users can also define custom validation rules for specific columns. Results are collected, persisted, and surfaced via applications or alerts, with the system optimising validation for cost and speed through smart partitioning and clustered columns.

An AgileData Engineering Pattern is a repeatable, proven approach for solving a common data engineering challenge in a simple, consistent, and scalable way, designed to reduce rework, speed up delivery, and embed quality by default.

If you want a copy of the pattern template head over to:

https://agiledata.substack.com/i/167964917/pattern-name

Discover more AgileData Engineering Patterns over at https://agiledata.substack.com/s/agiledata-engineering-patterns

If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn.