ETL vs ELT in Data Engineering – What’s the Difference and Which to Use

Difference Between ETL and ELT in Data Engineering

Introduction

Trying to understand ETL vs ELT in Data Engineering but getting confused?

You’re not alone.

Most people:

• Hear ETL in interviews
• Hear ELT in AWS projects
• See both used in data pipelines

But when asked the difference between ETL and ELT in real projects, they get stuck.

Because knowing definitions is not equal to understanding how data flows.

In this blog, you’ll understand:

• What ETL is
• What ELT is
• ETL vs ELT difference
• ETL vs ELT examples
• When to use ETL vs ELT

ETL vs ELT in data engineering refers to when transformation happens: ETL transforms data before loading, while ELT loads data first and transforms it later.

What is ETL in Data Engineering?

ETL stands for:

Extract → Transform → Load

Flow:

1. Data is extracted from source
2. Data is transformed
3. Data is loaded into storage

In simple terms:

Data is cleaned before storing.

1. Data comes from source
2. Processing happens before storage
3. Clean data is loaded into warehouse

Example:

API → Spark → Data Warehouse

What is ELT in Data Engineering?

ELT stands for:

Extract → Load → Transform

Flow:

1. Data is extracted
2. Data is loaded into storage
3. Data is transformed later

In simple terms:

Raw data is stored first and processed later.

1. Data comes from source
2. Loaded into data lake like Amazon S3
3. Transformations happen later using tools like AWS Glue

Example:

API → S3 → Glue → Analytics

ETL vs ELT Difference

ETL:

• Transform happens before loading
• Clean data is stored
• Used in traditional systems

ELT:

• Transform happens after loading
• Raw data is stored
• Used in modern cloud systems

ETL vs ELT

ETL:

• Extract → Transform → Load
• Processing before storage
• Less storage needed
• Less flexible

ELT:

• Extract → Load → Transform
• Processing after storage
• More flexible
• Works well with cloud

ETL vs ELT Example

ETL Example:

1. Data extracted from API
2. Processed using Spark
3. Loaded into data warehouse

ELT Example:

1. Data stored in Amazon S3
2. Processed later using AWS Glue
3. Used for analytics

When to Use ETL in Data Engineering

Use ETL when:

• Data must be cleaned before storage
• Storage is limited
• Strict data rules are required

When to Use ELT in Data Engineering

Use ELT when:

• Working with AWS or cloud platforms
• Using data lakes like S3
• Handling large volumes of data
• Need flexible transformations

Why ELT is Popular in Modern Data Engineering

• Cloud storage like S3 is cheap
• Data lakes are scalable
• Processing tools like Spark and Glue are powerful

So most modern AWS data pipelines use ELT.

How ETL and ELT Fit in AWS Data Engineering

ETL:

Used in older systems

ELT:

Used in modern AWS pipelines

Example:

S3 → Glue → Redshift

Common Mistakes

• Thinking ETL and ELT are same
• Using ETL where ELT is better
• Not understanding pipeline flow