ETL vs ELT — Why the “T” Moved to the End

It’s not just semantics. This shift changed how modern data pipelines are built

Hello friends,
Wherever you are in the world, thank you for being here.

This week, we’re breaking down a common question in data engineering:

What’s the difference between ETL and ELT — and why does it matter?

If you’re working with cloud data warehouses like Snowflake, BigQuery, or Redshift, this one’s for you.

🔧 What Is ETL?

ETL stands for:

• Extract data from source systems

• Transform it outside the warehouse

• Load it into the destination (usually a database or warehouse)

This is the traditional approach—built for a time when storage and compute were expensive, and transformations had to be handled before the data reached its destination.

🧰 Common in on-prem and older Hadoop/Spark ecosystems
🛠 Tools: Informatica, Talend, SSIS, PySpark scripts

⚡ What Is ELT?

ELT flips the order:

• Extract the data

• Load it into the warehouse first

• Transform it inside the warehouse (usually with SQL or dbt)

With ELT, your warehouse becomes the engine — you store raw data first, then clean, model, and enrich it inside your platform.

🛠 Tools: dbt, Snowflake SQL, BigQuery, Databricks Notebooks

🧠 Why the “T” Moved

Cloud warehouses changed the rules:
✅ Compute is cheap and scalable
✅ You can store raw data without huge costs
✅ Business logic changes fast — teams want flexibility
✅ Easier to audit and reprocess from raw sources
✅ SQL + version control = transparent pipelines

ELT lets you “land first, model later.”
Your team can move faster, experiment more, and debug with confidence.

⚖️ When ETL Still Makes Sense

Despite the cloud trend, ETL isn’t dead.

It’s still useful when:

• You’re processing massive data streams that need shaping before storage

• You need to cleanse or mask sensitive data before it ever touches a warehouse

• Your infra doesn’t support modern ELT workflows (yet)

So yes, it depends. But for most cloud-native teams, ELT wins.

🥘 Simple Analogy

ETL is like cleaning your groceries before putting them away.

ELT is like putting everything in the fridge, then deciding what to cook.

You get more flexibility and less upfront stress.

🧭 TL;DR

🔜 Next Week:

Data Lakes vs Lakehouses vs Warehouses — Which One Do You Need?

Thanks again for reading!
If you’re enjoying this series, feel free to share it with a teammate or buddy trying to navigate the modern data stack.

See you next Thursday,
— Raja 🙌