· Data Engineering · 1 min read
Why the Modern Data Stack is More Than Just Tools
Fivetran, dbt, and Snowflake are great, but buying them doesn't make you modern. We explore the mindset shift required for the Modern Data Stack.
It is tempting to think that you can buy a “Modern Data Stack.” You swipe your credit card for Fivetran, Snowflake, and dbt, and suddenly you are a data-driven company. Unfortunately, it doesn’t work like that.
It’s About Software Engineering Practices
The real revolution of the Modern Data Stack isn’t the cloud; it’s the application of software engineering principles to data:
- Version Control: Everything is code. Your SQL transformations (dbt models) live in Git, not inside a proprietary tool’s GUI.
- Testing: You don’t just run a query and hope. You have automated tests (schema tests, unique key tests) that run every time you deploy.
- CI/CD: You deploy automatically. No one is manually copying SQL scripts to the production server.
The ELT Shift
The shift from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) is crucial. By loading raw data first, we decouple the extraction from the logic. If our logic changes, we don’t need to re-fetch the data; we just re-run the transformation. This allows for iteration and agility.
Don’t Just Buy, Build
Adopting these tools without adopting the culture (Code Reviews, Documentation, Agility) will just lead to a faster, more expensive mess.
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