RAID
Rebtech AI Data Engineering
What is RAID?
Rebtech AI Data Engineering (RAID) is a methodology that combines generative AI with structured systems development to address this challenge. By documenting decades of experience in a framework of specialized AI Skills, data platforms can be developed with consistently high quality, dramatically shorter lead times, and lower risk of technical debt.
Why RAID?
Developing a modern data platform is often both time and resource intensive whether handled internally or using external consultants. With this in mind, Rebtech has developed the RAID concept.
By leveraging AI, we can rapidly build new data platforms while simultaneously optimizing existing solutions. The result is faster delivery, lower costs, and a platform that remains sustainable over time.
Benefits:
- Shorter development time
- Lower Total Cost of Ownership (TCO)
- High quality documentation, control, and governance
Customer reference cases:
Financial Services Client – Migration
Assignment
As part of a pre-study for a new data platform, a prototype was developed using RAID to demonstrate how a modern architecture could deliver the desired business value.
Approach
The work began with an analysis of the existing source code, followed by a target design based on a medallion architecture. Structured error handling was implemented in the Silver layer, while the existing Data Mart structure was preserved unchanged.
Results
Within three days, a solution was developed using a spec-driven approach—where the existing SQL code served as the functional specification to demonstrate how a migration of the current platform could be executed and structured in Microsoft Fabric.
The solution was built using a new architecture on a modern platform, with the existing SQL code serving as the functional specification.
Approach
The work began with data profiling and validation of requirements, followed by recommendations for any necessary adjustments. A solution design was then developed and reviewed by a Data Engineer. In the Silver layer, structured error handling was implemented, after which transformation code was generated and validated through automated testing.
Results
