ETL vs ELT: What’s the Difference and When to Use Each?

In the world of modern data engineering, two common approaches for data integration dominate the conversation: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).

Although the acronyms look similar, the difference in execution and architecture is significant. Choosing between ETL and ELT depends on the data sources, target systems, performance needs, and the tools in your stack.

In this post, we’ll compare ETL vs ELT and help you understand when each is the right choice.

What is ETL?

ETL stands for Extract, Transform, Load.

In this approach:

1. Data is extracted from various sources

2. Transformed into a usable format (cleaning, filtering, joining)

3. Then loaded into a target system like a Data Warehouse

ETL has traditionally been used when dealing with on-premise databases or systems with limited compute capabilities. Transformation happens before loading, often using dedicated ETL tools like:

• Talend

• Apache NiFi

• SSIS (SQL Server Integration Services)

• Informatica

When to use ETL:

• Your transformation logic is complex or sensitive

• You need to validate or clean data before storing it

• You’re working with traditional data warehouses

• Compliance or security requires pre-processing

What is ELT?

ELT stands for Extract, Load, Transform.

In this approach:

1. Data is extracted from the source

2. Loaded into the target system (often a cloud-based data warehouse)

3. Then transformed using the processing power of the warehouse itself

This pattern is gaining popularity with cloud-native platforms like:

• Snowflake

• Google BigQuery

• Amazon Redshift

• Azure Synapse Analytics

These platforms are designed to handle massive parallel processing, making in-warehouse transformation efficient and scalable.

When to use ELT:

• You use a modern cloud data warehouse

• You want to store raw data for future reprocessing

• You’re working with large volumes of structured and semi-structured data

• You prefer SQL-based transformations in the target system

Key Differences Between ETL and ELT

Both ETL and ELT aim to move data from source to destination, but they solve different challenges. ETL is ideal for systems where data must be cleaned and validated before storage. ELT shines in modern cloud environments, where scalability and raw data access are top priorities.

There’s no one-size-fits-all answer. The best approach depends on your data strategy, infrastructure, and performance requirements.