Practical articles on AI, Data Engineering, Metadata-Driven Development, Snowflake, Enterprise Architecture, and Modern Data Platforms.
Real-world engineering lessons, technical deep dives, and implementation guides by Amit Singh · 11 articles published
Showing 7 articles tagged #Data Engineering
A practical pattern for moving from source-to-target mappings and business definitions to a semantic Customer 360 model that business users can query safely.
Legacy ETL modernization is more than a code conversion exercise. Translating Informatica PowerCenter mappings to Snowflake SQL requires preserving transformation intent, surfacing hidden assumptions, and ensuring human approval before any artifact is released.
Organizations spend millions modernizing data platforms - yet many programs still struggle. The reason is surprisingly simple: technology changes, metadata remains. Here's why metadata-driven engineering changes the conversation.
Every migration starts from scratch. Teams rebuild business logic, mappings, transformations, documentation, and test cases - again and again. The technology changes. The business logic does not. What if the real asset is the metadata?
Data engineering teams spend countless hours on repetitive, metadata-driven work. The STTM already contains everything needed to build engineering deliverables. The challenge is that teams repeatedly translate that metadata into different formats.
Most people see DE Copilot as a code generation tool. The real engine sits in the middle - a metadata abstraction layer that transforms enterprise STTMs into unlimited engineering deliverables.
Can a metadata-driven engine understand and generate engineering artifacts from large, complex STTM documents without custom coding for every project? Here is what happened when we put it to the test.