If you’re learning data engineering, analytics, or working with data warehouses, you’ve probably come across the terms ETL and ELT.

They sound similar and they are. But they are used in different data architectures and solve problems in different ways.

In this guide, you’ll learn what ETL and ELT mean, how they differ, when to use each, and why the difference matters in modern data teams.

What Is ETL?

ETL stands for:

Extract → Transform → Load

How ETL Works

1. Extract data from source systems (databases, APIs, files)
2. Transform the data in a staging area
   • Cleaning
   • Aggregations
   • Formatting
3. Load the transformed data into a data warehouse

All transformations happen before the data reaches the warehouse.

Common ETL Tools

• Talend
• Informatica
• SSIS
• Apache NiFi

What Is ELT?

ELT stands for:

Extract → Load → Transform

How ELT Works

1. Extract data from source systems
2. Load raw data directly into the data warehouse
3. Transform the data inside the warehouse

Transformations are done using the warehouse’s computing power.

Common ELT Tools

• dbt
• Fivetran
• Stitch
• Airbyte

Key Difference Between ETL and ELT

The main difference is when and where transformations happen.

Feature	ETL	ELT
Transformation timing	Before loading	After loading
Transformation location	Outside warehouse	Inside warehouse
Data loaded	Cleaned data	Raw data
Infrastructure	Separate compute	Warehouse compute
Scalability	Limited	Highly scalable

Why ELT Is Popular Today

Modern cloud data warehouses like:

• Snowflake
• BigQuery
• Redshift

are extremely powerful and scalable.

Because of this:

• It’s faster to load raw data
• Transformations run inside the warehouse
• Storage is cheap
• Teams can reprocess data anytime

This makes ELT the default choice for modern data stacks.

When Should You Use ETL?

ETL is better when:

• You have limited warehouse compute
• Data must be transformed before storage
• Compliance requires clean data only
• Working with legacy systems
• On-premise data warehouses

ETL is still common in older enterprise systems.

When Should You Use ELT?

ELT is better when:

• Using cloud data warehouses
• Handling large volumes of data
• Want flexible transformations
• Need fast ingestion
• Supporting analytics and BI teams

Most modern data teams use ELT.

ETL vs ELT for Beginners

For beginners:

• ETL is easier to understand conceptually
• ELT is more relevant for modern jobs

If you’re learning data engineering or analytics today, focus on ELT first.

ETL and ELT solve the same problem which is moving data from source to warehouse — but they do it differently.

• ETL transforms data before loading
• ELT transforms data after loading

With the rise of cloud data warehouses, ELT has become the standard approach in modern data engineering.

Understanding both gives you a strong foundation in data pipelines.

FAQs