ELT vs ETL: definition & examples

ETL (extract, transform, load) transforms data before it reaches the analytics store. ELT (extract, load, transform) loads raw data first and transforms it inside the warehouse, usually with SQL. ELT suits cloud warehouses, which can run transformations in parallel on their own compute and keep the raw data available for re-modeling later.

What is the difference between ETL and ELT?

AWS summarizes it this way. ETL processes data from several sources against a set of business rules before central integration, while ELT loads data as it is and transforms it at a later stage, depending on the use case.¹ ELT uses the processing power and parallelism of cloud data warehouses to transform data in place.¹ Tools such as dbt are built for that “T”, turning raw warehouse tables into tested, modular models with SQL.²

	ETL	ELT
Transform step	In a pipeline or staging server, before loading	Inside the warehouse, after loading
Raw data kept?	Often not; only the transformed output lands	Yes, in raw or staging schemas
Planning needed up front	High: reports and rules must be defined first	Lower: load first, model iteratively
Good fit	Strict pre-load cleansing or masking; constrained targets	Cloud warehouses; fast-changing questions

Based on AWS’s comparison of ETL and ELT.¹

Example: Stripe data, both ways

With ETL, a job pulls Stripe invoices, converts currencies, computes MRR and writes one mrr_monthly table. If finance later wants MRR by plan, the job has to change. With ELT, the raw invoices land in the warehouse and an SQL model computes MRR. Adding “by plan” is a change to one model, and history can be recomputed from the raw data.

Common misconceptions

“ELT means no transformation before loading.” Light, mechanical steps such as type casting, deduplication and masking of sensitive columns often still happen on the way in.
“ELT replaced ETL.” ETL remains the right choice when data must be cleansed or minimized before it is stored anywhere.
“Either way, you have to copy the data.” Not always. Query pushdown runs the transformation at the source and returns only results.

How Kimo handles ELT

Kimo supports both patterns per source. In Cloud mode, connectors load data into Kimo’s managed storage on a sync schedule, using change data capture where the source supports it, and models transform it there. That is ELT. In Bridge mode, nothing is copied: Kimo Bridge pushes each query down to your database and returns only the results. The Cloud, hybrid or Bridge post helps you choose.

Change data capture: loading only what changed.
Data model: what the “T” produces.
Query pushdown: transforming at the source instead of copying.

Frequently asked questions

Is ELT cheaper than ETL?

Not automatically. ELT avoids a separate transformation server but spends warehouse compute and storage on raw data. Costs depend on data volume and how often models rebuild.

Can I mask sensitive data with ELT?

Yes. Mask or drop sensitive columns during extract or load, and restrict raw schemas with database permissions or row-level security.

What is reverse ETL?

Sending modeled data from the warehouse back into operational tools such as a CRM or ad platform. It is the opposite direction from ETL and ELT.

Sources

2 references

What’s the Difference Between ETL and ELT? (opens in a new tab)
Amazon Web Servicesaws.amazon.com
Definitions, planning differences, use of cloud warehouse compute.
What is dbt? (opens in a new tab)
dbt Labsdocs.getdbt.com
Transforming raw warehouse data into modular models with SQL.

External sources were accessed at the time of writing. Kimo product details, customers and figures in examples are illustrative unless a source is cited.

ELT vs ETL