ETL vs ELT: Which One Should You Actually Use?

If you've spent any time around data teams lately, you've probably heard the ELT versus ETL debate. Some people act like ETL is ancient history that belongs in a museum next to floppy disks. Others insist it's still perfectly relevant. The truth? Both approaches work—but for very different situations.

Let's cut through the noise and figure out which one actually makes sense for your team.

ETL means Extract, Transform, Load. You pull data from sources, clean and transform it on a separate processing server, then load the finished product into your warehouse.

ELT flips the last two steps: Extract, Load, Transform. You pull raw data and dump it straight into your warehouse, then transform it there using the warehouse's computing power.

The order matters because it fundamentally changes where the heavy lifting happens and who controls it.

ETL wasn't just popular by accident. For most of data warehousing history, storage was expensive and compute was limited. You couldn't just throw terabytes of raw data into a warehouse and figure it out later—that would bankrupt your budget.

So teams built ETL pipelines that did all the transformation work before data hit the warehouse. They'd run scripts overnight on dedicated ETL servers, clean everything up, and load only the final, polished datasets. This kept warehouse costs manageable and ensured data arrived in a consistent, usable format.

ETL also made sense when you had strict governance requirements. Transforming data before it entered the warehouse meant you could enforce business rules, mask sensitive information, and validate quality at the gate. Nothing messy ever made it inside.

However, this meant ETL pipelines were brittle. Every time a source system changed its schema or added a new field, someone had to update the transformation logic. Pipelines broke constantly, and fixing them required specialized skills. Data engineers spent more time maintaining code than delivering insights.

Snowflake, BigQuery, and Redshift showed up and rewrote the rules.

Suddenly, storage became dirt cheap, and compute scaled elastically. You could store petabytes of raw data for less than the cost of a coffee habit, and spin up massive processing power only when you needed it. The old constraints that made ETL necessary basically disappeared.

This opened the door for ELT. Why transform data on a separate server when your warehouse can do it faster and cheaper? Modern warehouses are purpose-built for analytics queries—they're really good at transforming data in place.

ELT also solved the flexibility problem. With raw data already in your warehouse, analysts can transform it however they need without waiting for engineering to update pipeline code.
Marketing wants to segment customers differently? They can write SQL and do it themselves.
Finance needs a new calculation? No ticket required.

The kicker: when source systems change, you don't need to rebuild transformation logic immediately. The raw data is already loaded. You just update your transformation queries when convenient.

Here's what nobody tells you: most organizations use both approaches simultaneously.

You might use ELT for cloud-based SaaS data where Fivetran loads everything raw, then dbt transforms it in Snowflake. But you also run ETL for that legacy Oracle database where you need custom Python scripts to clean data before loading.

Or you load raw data via ELT most of the time, but run certain high-compute transformations (like ML feature engineering) on Spark clusters before loading results back to the warehouse. That's technically ETL for those specific datasets.

The point isn't picking Team ETL or Team ELT and fighting about it on LinkedIn. It's understanding which approach fits each data source and use case.

Instead of asking "which is better," ask these questions:

Where does it make sense to do the work? If your warehouse can handle transformations efficiently, use it. If transformation logic is complex and benefits from specialized frameworks, do it externally.

Who needs to maintain this? ETL often requires data engineers. ELT with SQL-based tools like dbt empowers analysts. Match the approach to your team's skills.

How often does the source data change? Stable schemas work fine with ETL. Frequently changing sources benefit from ELT's flexibility.

What's the cost structure? Run the actual numbers. Sometimes, warehouse compute for complex transformations costs more than running ETL servers. Sometimes it's dramatically cheaper.

How quickly do you need the data? ELT typically delivers faster because you don't have to wait for transformation pipelines. But if transformations are simple, ETL might be just as fast.

ETL isn't dead, and ELT isn't a silver bullet. They're tools with different strengths and use cases.

ELT works beautifully in cloud-native environments with modern sources, SQL-fluent teams, and flexibility requirements. It's become the default for good reason—cloud economics and tooling finally caught up to make it practical.

ETL still matters for legacy systems, compliance-heavy industries, bandwidth-constrained environments, and complex transformations that don't fit the warehouse paradigm.

Most teams will use both. The trick is knowing which approach fits each situation rather than forcing everything through one pattern because it's trendy.