ETL vs ELT: A Beginner Data Engineer's Guide to the Modern Data Stack
Learn the key differences between ETL and ELT, when to use each, and which tools are best for cloud-native data pipelines.
ETL vs ELT: What Every Beginner Data Engineer Should Know
Feeling lost in the sea of data pipelines? You’re not alone. As data engineering shifts toward the cloud, new tools and processes are replacing the old guard. And right at the heart of this evolution is the ETL vs ELT debate.
In this guide, you’ll not only learn the difference between ETL and ELT, but also when to use each, which tools make sense in a modern stack, and how to get started the right way.
ETL vs ELT – Definitions & Workflows
What is ETL?
- Extract → Transform → Load
- Data is transformed before it’s loaded into the target system
- Traditionally used in on-premise systems and legacy architectures
What is ELT?
- Extract → Load → Transform
- Raw data is loaded first, then transformed within the data warehouse
- Designed for cloud-native, scalable architectures
Visual Summary:
ETL: Data warehouse gets clean data
ELT: Data warehouse gets raw data + logic for transforming
Why ELT Took Over Modern Data Engineering
-
Cloud Scalability
- ELT leverages powerful, elastic compute (e.g., Snowflake, BigQuery)
-
Flexibility & Agility
- Schema evolution and late-binding transformations are easier
-
Reduced Data Silos
- Load once, transform differently for analytics, ML, BI, etc.
-
Better Collaboration
- Tools like dbt allow analytics engineers to write SQL transformations in version-controlled projects
Pros & Cons – A Side-by-Side Comparison
| Feature | ETL | ELT |
|---|---|---|
| Transform Location | Before loading | After loading (in-warehouse) |
| Speed | Slower with large datasets | Faster in modern cloud warehouses |
| Flexibility | Less adaptable | Very adaptable |
| Tooling | Legacy ETL tools (e.g., Informatica) | Cloud-native tools (e.g., Fivetran, Airbyte) |
| Compliance | Easier to enforce pre-load rules | Requires strong SQL-based governance |
| Cost Efficiency | Compute used before warehouse ingestion | Utilizes scalable cloud compute |
| Skill Set Required | Scripting-heavy (Python, Java) | SQL-focused (especially with dbt) |
Choosing the Right Approach
When to use ETL:
- Regulatory or security constraints require transformed data to never touch the raw warehouse
- You’re working with legacy systems or limited compute resources
When to use ELT:
- You’re in a cloud-first environment (e.g., Snowflake, Redshift, BigQuery)
- You want faster iteration cycles and easier schema changes
- You want to democratize data access and transformation (e.g., with dbt)
- Include affiliate links here in each row’s Platform name or CTA.
My Journey from ETL to ELT
When I first started building data pipelines, I was stuck trying to manually transform everything before sending it to the warehouse. It was brittle, slow, and full of edge-case bugs.
Switching to ELT changed everything. I could load raw data into Snowflake, then experiment with transformations in dbt without breaking production. My team collaborated better. Deployment was faster. It just worked.
If you’re starting your data career today, start with ELT. It’s the most future-proof and scalable approach.
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Frequently Asked Questions
- Q: What’s the main difference between ETL and ELT?
- A: ETL transforms data before loading it into storage. ELT loads raw data first, then transforms it inside the warehouse using tools like dbt.
- Q: Is ELT better than ETL?
- A: Not always. ELT is better for cloud-native environments and agility, but ETL is better for security-first, regulated, or legacy contexts.
- Q: Can I mix ETL and ELT?
- A: Yes. Many teams use a hybrid approach where sensitive data is pre-cleaned (ETL) while the rest is handled via ELT.
- Q: Is ELT just SQL?
- A: Mostly, yes. Tools like dbt are SQL-based. But you can use Python, JavaScript, or Spark for transforms in some platforms too.
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