After working on multiple modernization initiatives, I’ve noticed a pattern:

Organizations spend months discussing:

• Databricks vs Snowflake
• Spark vs SQL
• Batch vs Streaming
• Airflow vs Managed Orchestration

But the biggest challenge is usually somewhere else.

It’s metadata.

Business rules, source-to-target mappings, data definitions, lineage, data quality requirements, and transformation logic often exist across spreadsheets, legacy ETL tools, tribal knowledge, and documentation.

When moving from legacy platforms (Informatica, DataStage, SSIS, Teradata, Netezza, Oracle) to modern platforms like Databricks, teams frequently end up rebuilding the same knowledge repeatedly.

This led me to explore a different question:

What if modernization started with metadata instead of code?

Instead of migrating individual artifacts, can we standardize metadata into a Canonical Metadata Model and generate:

✅ SQL
✅ Data Quality Rules
✅ Technical Specifications
✅ Data Dictionaries
✅ ER Diagrams
✅ dbt Models
✅ Databricks Notebooks
✅ Other engineering deliverables

from a single metadata representation?

I wrote about this concept here:

https://dev.to/amising6/from-legacy-data-platforms-to-modern-data-stacks-why-metadata-matters-more-t...

Curious how others approach modernization projects:

Do you see technology migration as the hardest part, or is understanding and preserving business metadata the bigger challenge?

#Databricks #DataEngineering #DataArchitecture #Lakehouse #DataGovernance #Metadata #ModernDataStack #DataPlatform #ApacheSpark #AnalyticsEngineering