The difference between ETL and ELT is where the data gets transformed. ETL (extract, transform, load) cleans and reshapes data on a separate processing server before loading it into the warehouse. ELT (extract, load, transform) loads raw data straight into the target warehouse or data lake and transforms it there. Microsoft's architecture guidance says ELT differs from ETL solely in where the transformation takes place, and AWS calls ELT the standard choice for modern analytics.
If you've been handed a data project, or you're choosing a tool to pull sales, marketing and finance data into one place, these two terms come up in the first meeting. They sound almost identical. The order of two letters changes cost, speed and how your team works, so it's worth getting clear.
ETL vs ELT side by side
| ETL | ELT | |
|---|---|---|
| Order of steps | Extract, transform, load | Extract, load, transform |
| Where transformation happens | On a secondary processing server | Inside the target data warehouse or lake |
| What gets loaded | Only data that's already transformed | Raw data in its natural state |
| Best suited to | Structured data in rows and columns | All data types, including unstructured data like images or documents |
| Speed as data grows | The extra step before loading is harder to scale | Loads directly and transforms in parallel using warehouse compute |
| Setup | Analysts plan reports and data structures up front | Fewer systems to maintain |
| Privacy work | Developers often build custom solutions, such as masking personal data | Uses security features built into the warehouse |
That table follows AWS's own comparison. The key idea is simple: ETL moves data once it's ready, ELT moves everything first and shapes it later, as many times as you need.
How ETL works
- Extract: collect raw data from sources such as databases, files, SaaS apps, sensors or application events.
- Transform: clean, sort, filter, join, deduplicate and validate it on a separate engine, often using staging tables.
- Load: store the finished data in the target system so reporting tools can use it straight away.
Microsoft adds a useful detail: the three ETL phases often run in parallel to save time. While data is still being extracted, transformation can start on what's already arrived, and loading can begin on the prepared data.
How ELT works
- Extract the raw data from your sources.
- Load it unchanged into a cloud data warehouse or data lake.
- Transform it inside the warehouse whenever you need it, using the warehouse's own processing power.
AWS explains the history in one line: ETL has been around since the 1970s and grew with data warehouses, then cloud storage made it possible to keep huge amounts of raw data and analyse it later. That's what made ELT the modern default.
When ETL is still the better choice
ELT isn't always right. AWS and Microsoft both list cases where transforming first still wins:
- Legacy databases or third party sources with fixed formats, where you transform once and reuse the result.
- A target system with limited power, where Microsoft says you may need to offload heavy transformations.
- Complex business rules that need a specialised transformation engine.
- Regulatory or compliance rules that require audited staging before data is loaded.
- Internet of Things sensor streams, where AWS says filtering, averaging or cleaning data at the edge before sending it to the cloud often makes sense.
AWS also notes that many teams use both: ETL pipelines for some sources and ELT for the rest.
A third term: reverse ETL
You may also hear about reverse ETL. Microsoft describes it as moving transformed, modelled data from analytical systems back into operational tools and applications, for example sending a customer score from your warehouse into your CRM. It's the same plumbing running in the other direction.
A simple example
Say you run an online shop and want one dashboard with orders, refunds and email sign ups. With ETL, you'd decide the report first, build a process that cleans and joins those sources on a separate server, and load only the finished tables. With ELT, you'd copy the raw orders, refunds and sign ups into your warehouse as they are, then write transformations inside the warehouse to build the dashboard tables. When you later want a new report, ELT lets you go back to the raw data you already hold.
Questions to ask before you choose
- Where will the data end up? A modern cloud warehouse or lakehouse with elastic compute favours ELT, according to Microsoft's guidance.
- Does it contain personal data that must be masked before anyone can query it? That can push you towards transforming first.
- Are your sources structured tables, or do you also have documents, images and event streams? AWS says ELT handles all data types.
- How often will the questions change? If you'll keep asking new things of the same data, ELT keeps the raw history available.
ELT leans on the warehouse doing the heavy lifting, and our BigQuery vs Snowflake guide covers two of the best-known ones.
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Choosing tools for either approach
Whichever approach you choose, the tool you pick shapes how much work it takes. If you're comparing options, start with my guide to the best data integration tools, then look at the best ETL tools if you need heavier transformation before loading.
My verdict
For a small or growing business putting data into a cloud warehouse, ELT is the sensible default: fewer moving parts, faster loading and the freedom to reshape data later. Reach for ETL when you're working with legacy systems, strict compliance staging or a target that can't do the heavy lifting itself. Don't let the jargon decide for you. Decide where you want the work to happen, and the acronym follows.
Frequently asked questions
What is the main difference between ETL and ELT?
Where transformation happens. ETL transforms data on a separate server before loading it, and ELT loads raw data first and transforms it inside the target warehouse.
Is ELT better than ETL?
AWS calls ELT the standard choice for modern analytics, but ETL still suits legacy sources, constrained target systems and compliance rules that need staging before loading.
Is ELT faster than ETL?
AWS says ELT is faster because it loads data directly and transforms it in parallel using the processing power of the cloud warehouse.
Can you use ETL and ELT together?
Yes. AWS notes that teams may set up ETL pipelines for some sources and use ELT for the rest.
What is reverse ETL?
Microsoft describes reverse ETL as moving transformed, modelled data from analytical systems into operational tools and applications.
