Data Engine Thinking is a structured, software- and methodology-independent approach to designing and managing data solutions. Rather than hand-building a data solution once, you build the engine that delivers it — reference architectures defined in layers and areas, reusable design and solution patterns, and metadata-driven automation — so the solution can evolve as requirements, teams, and technology change.
This organisation is the home of the open-source frameworks, schemas, and examples that support the approach, as described in the book Data Engine Thinking.
| Repository | What it is |
|---|---|
| samples | A curated, living library of sample code, design patterns, and solution patterns supporting the approach. Browse it at docs.dataenginethinking.com. |
| data-solution-automation-metadata-schema | A generic interface exchange format for data solution automation and code generation: the JSON Schema definition, a class library, and worked examples. See the schema documentation. |
| DIRECT | The Data Integration Run-time Execution Control Tool — a data logistics control framework to monitor, log, audit, and control data integration processes. |
| testing-framework | An open-source testing framework that automates the validation of a data solution with reusable data quality tests. |
| book-notes | The central hub for reporting, tracking, and resolving typos and content issues for the book. |
Tip
The Data Engine Thinking book is out! Get it now at the Data Engine Thinking website.
Note
Several repositories moved here from the data-solution-automation-engine organisation. All existing links, clones, and Git remotes redirect automatically to their new home.
These repositories are intentionally living: improved, expanded, and refined as new lessons and practices emerge. If you spot a gap, find an issue, or want to add a pattern — small edits are just as valuable as new contributions — pull requests are very welcome.