Why I’m writing this
When I started in data, nothing was elastic.
Storage had limits. Compute had limits. And if you broke the warehouse, you were going to hear about it.
So we were careful.
We shaped data before it earned the right to live in production.
That discipline created ETL.
Then the cloud happened.
And the order flipped.
ETL – Extract → Transform → Load
ETL means:
- Extract from source systems
- Transform in staging
- Load only clean, structured data
Think traditional enterprise stacks:
- SQL Server
- Oracle Database
- SQL Server Integration Services
- Informatica PowerCenter
The warehouse was protected.
You didn’t dump raw logs in there. You didn’t “figure it out later.” You shaped it first.
Very DBA energy.
It was built for a world where:
- Storage was expensive
- Compute was limited
- Governance meant strict control
The mindset:
Clean it before it enters the building.
ELT – Extract → Load → Transform
ELT flips the order:
- Extract
- Load raw
- Transform inside the platform
Think modern platforms:
- Snowflake
- Databricks
- Amazon Redshift
- BigQuery
Now the warehouse (or lakehouse) is the transformation engine.
You land raw data.
Bronze → Silver → Gold.
You keep the messy stuff — because you might need it later.
This only works because:
- Storage became cheap
- Compute became elastic
- Reprocessing became viable
The mindset:
Store first. Refine later.
Very cloud-native energy.
What Actually Changed?
Not the letters.
The economics.
| ETL World | ELT World |
|---|---|
| Hardware constrained | Elastic compute |
| Schema-first | Data-first |
| Protect the warehouse | Use the warehouse |
| Transform outside | Transform inside |
| Risk-averse | Iteration-friendly |
ETL was discipline born from constraint.
ELT is flexibility born from scale.
Which Should You Use?
It depends on:
- Regulatory pressure
- Data volume
- Cost model
- Team maturity
- Failure tolerance
If you’re running tightly governed legacy workloads, ETL still makes sense.
If you’re building modern platforms in AWS or Databricks, ELT is often the natural pattern.
This isn’t religion.
It’s architecture.
The Data & Grit Take
Good engineers don’t argue acronyms.
They understand trade-offs.
ETL wasn’t primitive. ELT isn’t revolutionary.
They are reflections of the infrastructure available at the time.
The job isn’t to follow fashion.
It’s to design systems that survive reality.
Gareth Winterman