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 11 articles
Banks already know how to capture changes at the transaction level. But what happens when a downstream system needs to know that a customer's risk score moved from 42 to 67 - not which five transactions caused it? That is the architectural gap CDF on Materialized Views is designed to close.
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.
Source-to-Target Mappings sit at the center of every data pipeline, yet they are treated as throwaway documents. Here is why that needs to change - and what becomes possible when it does.