One pattern I keep seeing in data engineering projects:
STTM is treated as documentation.
But in reality, STTM can become much more than that.
A well-structured Source-to-Target Mapping can act as a metadata contract between business, engineering, QA, and analytics teams.
Once the metadata is standardized, it can drive:
- DDL generation
- SQL / PySpark transformation logic
- Data quality rules
- Test cases
- Documentation
- Lineage
- Deployment readiness checks
This is especially relevant in platforms like Databricks, where metadata-driven development can help teams move from manual pipeline creation toward repeatable, governed data product delivery.
The future of data engineering may not be only about writing more code.
It may be about designing better metadata contracts that generate the right code, tests, and controls consistently.
#Databricks #DataEngineering #Metadata #STTM #DataQuality #Lakehouse
Amit Kumar Singh
Lead Data Engineer | AI-Assisted Data Engineering
