What’s the Difference Between ETL and ELT on AWS?

What’s the Difference Between ETL and ELT on AWS?

Introduction

AWS Data Engineering is becoming one of the most important skills in today’s technology world. Every company collects data from websites, mobile apps, payments, customer forms, and social media. But raw data is not useful unless it is cleaned and arranged properly. In the middle of learning an AWS Data Engineering Course, students often hear two common words — ETL and ELT. These two methods help move and prepare data, but they work in different ways.

What’s the Difference Between ETL and ELT on AWS?

What is ETL?

ETL means:

• Extract
• Transform
• Load

In ETL, data is first collected from different sources. Then it is cleaned and changed into the correct format. After cleaning, it is loaded into a data warehouse.

Easy Example

Imagine you are preparing rice for cooking:

1.     You take rice from the bag (Extract).

2.     You wash and clean it (Transform).

3.     You put it into a cooker (Load).

This is exactly how ETL works.

ETL on AWS

In AWS, ETL usually works like this:

• Data is stored in Amazon S3
• It is cleaned using AWS Glue
• Finally, it is stored in Amazon Redshift

Here, transformation happens before loading into the warehouse.

 

What is ELT?

ELT means:

• Extract
• Load
• Transform

In ELT, data is first collected. Then it is directly stored in the data warehouse. After that, it is cleaned and transformed inside the warehouse.

Easy Example

Imagine buying fruits:

1.     Bring fruits home (Extract).

2.     Keep them in the fridge (Load).

3.     Wash and cut them only when needed (Transform).

This is ELT.

ELT on AWS

In ELT systems:

• Data is stored in Amazon S3
• Loaded into Amazon Redshift
• Transformed using SQL inside the warehouse

Sometimes heavy processing is done using Amazon EMR.

 

Main Difference Between ETL and ELT

Feature	ETL	ELT
Order	Extract → Transform → Load	Extract → Load → Transform
Speed	Slower for large data	Faster for big data
Storage	Only clean data stored	Raw + clean data stored
Flexibility	Less flexible	More flexible
Best For	Traditional systems	Cloud-based systems

 

Why Companies Use ETL

ETL is used when:

• Data must be cleaned before storage
• There are strict rules and policies
• Storage space is limited
• Data quality is very important

Banks and healthcare companies often prefer ETL because they must follow strict regulations.

 

Why Companies Prefer ELT in AWS

Modern companies handle huge amounts of data every second. Cloud platforms provide:

• Low-cost storage
• High computing power
• Easy scaling
• Fast processing

Because of these benefits, ELT has become very popular.

If you learn from an AWS Data Engineering Training Institute, you will understand how ELT helps companies analyze data faster and make quick decisions.

 

Real-Life Scenario

Imagine an online shopping website. It collects:

• Customer details
• Orders
• Payments
• Product views
• Reviews

With ETL, the company cleans and filters everything before storing it.

With ELT, it stores all data first, even if it is messy. Later, when analysts need specific reports, they transform only the required data.

ELT saves time because loading happens immediately.

 

Performance Comparison

ETL Performance

• Takes more time before loading
• Needs a separate transformation server
• Good for structured data

ELT Performance

• Loads data quickly
• Uses warehouse power for transformation
• Best for big and unstructured data

 

Cost Comparison

In older systems, storage was expensive. So companies cleaned data before storing it.

Today, cloud storage like Amazon S3 is affordable. So companies store raw data first and transform later. This makes ELT cost-effective for big data projects.

 

Skills Required to Work on ETL and ELT

To work on both methods, you need:

• Basic SQL knowledge
• Understanding of data pipelines
• Cloud service knowledge
• Problem-solving skills

If you are searching for practical exposure, enrolling in AWS Data Engineering training in Hyderabad can help you work on real-time projects using both ETL and ELT models.

 

When to Choose ETL

Choose ETL if:

• Data needs strict cleaning rules
• Compliance is very important
• Data size is manageable
• You want only processed data stored

 

When to Choose ELT

Choose ELT if:

• You handle large data
• You need quick data loading
• You want future flexibility
• You use modern cloud warehouses

 

Advantages of ETL

• Clean data before storage
• Better control over quality
• Suitable for regulated industries

Advantages of ELT

• Faster loading
• Stores complete raw data
• Better for advanced analytics
• Works well in cloud systems

 

Simple Summary in One Line

ETL cleans data before storing it.
ELT stores data first and cleans it later.

 

FAQs

1. Is ETL still used today?

Yes. Many companies still use ETL, especially where strict rules are required.

2. Why is ELT popular in cloud platforms?

Because cloud systems provide powerful storage and processing, making ELT faster and flexible.

3. Which is easier to learn, ETL or ELT?

Both are easy if explained with simple examples. The logic is almost the same; only the order changes.

4. Do companies use both ETL and ELT?

Yes. Some projects use ETL for sensitive data and ELT for large analytical data.

5. Is coding required for ETL and ELT?

Basic SQL is required. Some tools reduce heavy coding work.

 

Conclusion

Understanding the difference between ETL and ELT is very important in modern data projects. Both methods help move data from one place to another and prepare it for analysis. The main difference is the order in which transformation happens. Cloud platforms have made ELT more common, but ETL still has strong importance. Learning both approaches will make you confident and ready to work in real-world data environments.

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