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

Overview

The debate of ETL vs ELT basically boils down to its sequence of operations: 

• ETL (Extract, Transform, Load): This method transforms Data before it enters its destination, and is best used for structured data stored in traditional data warehouses. 
• ELT (Extract, Load, Transform): This method transforms the data within its target system and is thus perfect for modern projects that use cloud data platforms for storage. 

In this blog, I will break down the major differences between ETL and ELT, and help you choose the right data migration method for your use case. 

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Data is the new-age commodity that drives our tech-driven world. 

With such large quantities of data moving around daily, a reliable data migration strategy remains very important for any modern company’s functioning. 

Choosing the wrong data migration strategy for your business can lead to productivity loss, daily bottlenecks, and wastage of valuable resources. 

Don’t worry, as I’m here to help you make your choice and understand the fundamental dynamics of ETL vs ELT quickly and easily! 

This will be critical to ensure you only invest in the best data migration practice that suits your needs.

Let’s dive in! 

• ETL vs ELT - Quick Overview
• What is ETL? (Extract, Transform, Load)
• What is ELT? (Extract, Load, Transform)
• ETL vs ELT: Detailed Breakdown of Key Differences
• How to Choose Between ETL and ELT?
• Final Verdict: ETL or ELT
• ETL vs ELT: FAQs

Here is a quick overview of the key differences that separate ETL and ELT:

ETL is a time-tested legacy data transformation process. It involves data being first extracted from the source systems, and then being transformed through the following steps:
 

1. Cleaning the Data
2. Enriching the Data
3. Aggregating the Data

This is done using a separate processing server. After this transformation is done, the data is loaded into the target data warehouse.

Using this “transformation-before-load” approach in ETL ensures that only high-quality usable data is filtered and entered into the new storage.

In my opinion, this reliability makes ETL a cornerstone of traditional data migration services. 

Let me walk you through some of its key characteristics.

Key Characteristics of ETL

Best For: I believe ETL is ideal if your organisation requires strict data governance, traditional data warehouses, and teams working mainly with structured data.

• Governed Data Quality: Using ETL, your rules are defined beforehand, and the data is standardized and validated accordingly before it reaches the target warehouse. 

This ensures that the data is already compliant and validated, making the data transformation process a popular choice for regulated industries like finance and healthcare. 

• Optimised for Structured Data: ETL has proven to be a reliable method for transferring structured, relational data that is neatly categorised into tables and schemas. 

Where ETL struggles with its efficiency is in moving massive volumes of unstructured data. 

• Mature Tooling: As a traditional choice, the ETL ecosystem is mature and enjoys the support of a massive library of powerful tools, such as Informatica and Talend. 

This is beneficial as many established data migration services already have close integration with these popular platforms.  

Thus, ETL has proven to be a reliably powerful approach to data migration that benefits greatly from its traditional legacy. 

However, be aware that its rigid processes can cause unavoidable bottlenecks in today’s rapidly evolving world of big data and dynamic analytics. 

Best For: ELT is the ideal choice if your company mainly uses data stacks that leverage modern cloud platforms, agile data projects, and teams working primarily with unstructured data. 

As a modern data migration method, ELT is powered by the bleeding-edge power and scalability of cloud data platforms.
 

Data is seamlessly extracted from its source and loaded immediately into highly scalable destination systems like a cloud data warehouse or lake.

In ELT, data transformation happens AFTER the data is loaded, using the power of the target system itself for its filtration and processing.

Key Characteristics of ELT

• Unmatched Speed and Agility: As ELT involves loading raw data first, this data migration process is a lot quicker when compared to ETL. 

Analysts on the target site can then transform the data on the fly, enabling seamless exploration.

• Handles All Data Types: ELT is the best migration option when it comes to unstructured data, with its unique “schema-on-read” approach providing incredible flexibility for moving data like JSON, logs, etc.
  
  
• Harnesses Cloud Scalability: The major heavy lifting of transformation in ELT is done by scalable cloud engines like Snowflake, BigQuery, and Redshift.
  

They eliminate the need for a dedicated transformation server, significantly reducing infrastructure costs and processing times. 

If your organisation has already embraced a modern cloud-first approach to data storage, I would definitely recommend ELT as a go-to data migration strategy. 

It offers speed and flexibility without suffering from the drawbacks of favoring defined data structures.

1. Core Philosophy & Data Handling 

ETL: Operating on a “schema-on-write” philosophy, data is required to be cleaned, structured, and conformed before it is stored at its target destination. This ensures better reliability and consistency, but sacrifices speed and agility of the data migration process. 

ELT: With its “schema-on-read” philosophy, raw data is loaded immediately to the target site, and structure is applied after it is received. This ensures that the original data is preserved completely and offers maximum flexibility for future analysis. 

Which is Better?

Between ETL vs ELT, the choice depends entirely on your use case. I can verify that ETL is excellent if you plan to enforce strict and regulated data models before migration. 

Meanwhile, ELT is better for data exploration, ML pipelines, and adapting to a changing business ecosystem.

2. Performance & Scalability

ETL: Being a traditional method, ETL’s transformation step remains its biggest bottleneck, especially when it comes to large unstructured databases. Scaling requires hefty infrastructure costs as a dedicated ETL server will need to be replaced. 

ELT: Leveraging the vast scalability of modern cloud data platforms, transformation jobs can run parallel to the data migration process. This ensures minimal bottlenecks and offers seamless transformational power scalability in accordance with your storage requirements. 

Which is Better?

If performance and scalability are your focus during data migration, only consider ELT as your primary choice. 

With its seamless integration with cloud-computing capabilities, ELT provides vastly better speeds and scalability in comparison to ETL.

3. Flexibility & Agility

ETL: Any changes to transformational logic or organisational rules will often require restarting the data reprocessing from the start. This leads to a slow and resource-intensive process of data migration. 

ELT: Offering superior agility, a transformational logic change is just a simple SQL query away. This makes the iterative development of the data much quicker and seamless. 

Which is Better? 

I recommend ELT for businesses whose requirements evolve quickly and where data exploration is a key activity. 

4. Cost & Infrastructure

ETL: Requires investment and maintenance of proprietary ETL servers and software, leading to higher upfront and operational costs.

ELT: The cost model is entirely dependent on a consumption-based pricing of the cloud platform, offering cheaper pricing in most scenarios, as you only have to pay for the computing power and storage you are using. 

Which is Better?

Want to minimize your infrastructure management costs and enjoy a more hands-off approach to your data migration strategy? 

Look no further, as ELT incorporates flexible pricing models that will definitely benefit your company’s bottom line.

5. Skillset & Maintenance

ETL: Requires specialised skills to manage the complex ETL tooling and server infrastructure.

ELT: Leverages SQL skills that most data analysts and engineers already understand, reducing the steep learning curve and broadening the number of people who can work with the organisation’s data pipeline. 

Which is Better?

If you are looking to make multiple team members participate in your data transformation process, ELT is your best option to cut down on their learning curve and make the entire data migration process much faster. 

Still confused about the basics of Data Migration? Read my comprehensive guide on different types of data migration. 

Still confused about which data integration pattern is the best option for your needs? Let me help with some relatable scenarios: 

Choose ETL When:

• You are migrating to a legacy on-premise data warehouse. 
• You operate in an industry that is heavily regulated and follows strict data governance and compliance demands.
• Your data is mostly structured, and reporting needs are already well-defined. 
• Data quality and consistency are your highest priority. 

Choose ELT When:

• Your data migration strategy is cloud-first and uses platforms like BigQuery or Azure Synapse. 
• You work primarily with large volumes of unstructured data. 
• Speed is critical, and you need raw data available for exploratory analysis. 
• You require a more agile and flexible approach for your data teams. 

Need more help? Check out my blog on the top 10 data migration considerations. 

Designing an effective data architecture is a multi-step process that requires the right data integration pattern to be sustainable and reliable for the future. 

Through this blog, I hope I’ve helped you understand the major differences between ETL vs ELT and the numerous pros and cons of these two data migration methods.

But an even bigger challenge during this process is its implementation.

Building a robust, reliable pipeline is vital to ensure your team can leverage a new data architecture more effectively.

And that’s where Augmented Tech Labs can help!

As an experienced data and analytics consultancy, our team can offer certified data architects who specialise in both ETL & ELT.

They are trained to help you navigate this journey seamlessly and ensure that you maximise your performance and ROI.

So, if you need more help in deciding the right data migration services, connect with us today to receive a free consultation! 

1. What is the main difference between ETL and ELT?

The main difference in the ETL vs ELT debate is the sequence of operations: ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it inside the target system.

2. When should I consider professional data migration services?

Engage a professional service when designing your overall data migration strategy, implementing complex hybrid pipelines, or when you lack the in-house expertise to ensure optimal performance and governance.

3. Is ELT replacing ETL?

Not exactly. ELT is becoming the dominant pattern for new, cloud-native projects due to its flexibility and scalability. However, ETL remains critically important for governed, compliance-heavy use cases with structured data.

4. What are the key data migration best practices when using ELT?

Key data migration best practices for ELT include: implementing strong data cataloging and governance around your raw data, monitoring cloud costs closely, and ensuring your team has strong SQL skills.

5. Can ETL and ELT be used together?

Absolutely. Many organizations use a hybrid approach, using ETL for governed, mission-critical data marts and ELT for agile data exploration in a data lake, forming a modern "data lakehouse" architecture.