Automated Load Patterns based on Source Data Profiles, AgileData Engineering Pattern #4 - Episode #70

Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern of Automated Load Patterns based on Source Data Profiles.

 

The Automated Load Patterns based on Source Data Profiles pattern automatically profiles incoming data to determine its optimal loading method. It classifies data as either event data (immutable, append-only) or change data (evolving records) by analysing characteristics like unique key volume and column names over time. This automated classification then dictates the load strategy: an efficient partition replace for event data or a cost-effective upsert (end-dating historical records) for change data. This capability removes the manual burden and accelerates data onboarding, providing a trustworthy and efficient process even when the data type is initially unknown.

 

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/168912285/pattern-name

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

 

If you want to join us on the next podcast, get in touch over at https://agiledata.io/podcasts/#contact

 

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

 

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