Inspiration
Inspiration
After more than 15 years in data engineering, I noticed a recurring pattern across nearly every project.
Teams spend significant time translating Source-to-Target Mapping (STTM) documents into engineering deliverables:
- Snowflake DDL
- SQL transformations
- Data dictionaries
- Technical specifications
- Data quality rules
- Test artifacts
- Documentation
Much of this work is repetitive, predictable, and manually recreated from project to project.
I wanted to explore a simple question:
Can a metadata-driven platform transform a single STTM into multiple engineering deliverables automatically?
That question became DE Copilot.
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What It Does
DE Copilot converts enterprise STTM metadata into engineering artifacts through a Canonical Metadata Model.
Current capabilities include:
- Snowflake DDL Generation
- Snowflake SQL Generation
- Data Dictionary Generation
- Technical Specification Generation
- Data Quality Rule Generation
- AI-Powered Metadata Analysis
Instead of generating each artifact independently, DE Copilot creates a normalized metadata representation and uses that model to generate outputs.
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How We Built It
The platform consists of several layers:
- STTM Upload & Parsing
- Metadata Normalization
- Canonical Metadata Model
- Artifact Generation Engine
- AI Analysis Layer
The Canonical Metadata Model acts as an abstraction layer between enterprise mapping documents and downstream engineering deliverables.
This architecture allows the same metadata to generate multiple outputs without rewriting logic for every target platform.
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Challenges
The biggest challenge was not code generation.
The hardest problem was designing a metadata model capable of interpreting different STTM formats consistently.
Enterprise mapping documents vary significantly between organizations.
Creating a reusable metadata abstraction layer required identifying the common patterns shared across these formats while maintaining enough detail for downstream generation.
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What We Learned
The project validated an important hypothesis:
A normalized metadata layer can serve as the foundation for engineering automation.
Once metadata is standardized, the same model can generate:
- SQL
- PySpark
- dbt
- Airflow
- Azure Data Factory
- AWS Glue
- Data Quality Frameworks
- Test Artifacts
- Documentation
The architecture becomes platform-independent.
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Future Vision
The long-term goal is to transform enterprise metadata into complete engineering deliverables.
Future roadmap includes:
- PySpark Generation
- Databricks Notebooks
- Delta Live Tables (DLT)
- dbt Models
- Airflow DAGs
- Azure Data Factory Pipelines
- AWS Glue Jobs
- Great Expectations
- SIT/UAT Test Workbooks
- Knowledge Discovery Copilot
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Live Platform
Website:
https://dataengineeringcopilot.com
Canonical Metadata Model Article:
https://dataengineeringcopilot.com/blog/canonical-metadata-model-engine-behind-de-copilot
Enterprise STTM Stress Test:
https://dataengineeringcopilot.com/blog/stress-testing-de-copilot-enterprise-sttm
