ETL vs ELT: Key Differences, Pros & Cons, When to Use What?

Success today depends on making smart decisions quickly and reacting to market changes in real time. That's why businesses collect data from a variety of sources, including social media, customer interactions, web apps, and portable telematics.

However, all that data is only useful if it's managed effectively. To do this, most companies rely on one of two key methods: ETL or ELT. Though the names sound similar, the processes are quite different.

Understanding how ETL and ELT work is essential to building an efficient data strategy. In this guide, we'll break down each method, highlight their pros and cons, and help you decide which one best fits your business needs.

ETL (Extract, Transform, Load)

The ETL pipeline involves three key steps:

• Extraction – Data is collected from multiple sources in various formats.
• Transformation – The data is cleaned, structured, and converted into the required format.
• Loading – The processed data is then moved into a centralized data warehouse or storage system.

To learn more, refer to this detailed guide: https://skyvia.com/learn/etl-pipeline-meaning

Example: Using ETL for OLAP Data Warehouses

OLAP systems are designed for complex analytical and statistical queries. However, they require clean, well-structured data to function effectively. In this context, ETL is the ideal approach to prepare and deliver high-quality data to the warehouse.

ELT (Extract, Load, Transform)

ELT is a more modern approach that also involves three key steps:

• Transformation – The data is processed and transformed within the storage system as needed.
• Extraction – Data is gathered from multiple sources.
• Loading – The raw, unstructured data is quickly loaded into a data lake or warehouse.

Example: Using ELT for Data Lakes

Data lakes are designed to store large volumes of diverse data, including tables, images, logs, and more. ELT is well-suited for this setup, as it enables fast loading of raw data and efficient storage without the need for immediate transformation.

The Limitations of ETL

With market challenges becoming more intense, businesses must stay alert to even the slightest shifts in market trends and clients' behavior. To stay competitive, they increasingly rely on data from a wide range of sources — creating a growing need to process large volumes of information efficiently. This demand is now being met by modern cloud storage solutions, which offer user-friendly interfaces and a wide array of powerful tools.

At this stage, ETL systems are gradually losing popularity due to several key limitations:

• Extensive data preparation is required upfront, which becomes increasingly difficult as data volume and complexity grow unpredictably.
• ETL-compatible storage systems are designed for structured, pre-transformed data. As a result, handling large unstructured files—such as videos, images, or sensor data—can be slow and error-prone.
• Managing data pipelines across multiple sources often leads to higher operational costs and added complexity.

These limitations have led companies to seek more effective solutions for handling real-time monitoring and data processing.

Growing Popularity of ELT

The increase in fast data management importance and complexity has become a crucial impulse towards massive ELT integration. The method ensures the use of a so-called 'Schema-on-Read' approach. It means that information is stored in its original form, and the schema is applied only when it's needed.

This method is complementary to modern cloud warehouses, which ensures the ability to run several tasks simultaneously, to decrease job setup time, and adjust data resources to fast-changing business demands.

ETL vs. ELT: What Sets Them Apart?

• ETL processes data before loading it into a database. This approach often requires advanced IT skills and significant time investment, leading to higher costs and potential delays. While it can still be effective for smaller businesses with limited data sources and simpler storage needs, it's less suited for modern, large-scale data environments.

• ELT, on the other hand, is built for handling large and varied datasets. It stores raw data immediately and processes it only when needed. This Schema-on-Read approach helps reduce both time and cost. As self-service data warehouses continue to evolve, ELT empowers employees across departments to work with data more independently and efficiently.

ETL and ELT Comparison

Section	ETL	ELT
Interpretation	The received data is prepared and restructured before being loaded into storage.	The information can be loaded promptly and transformed whenever it's needed.
Operational Time	Operational process is slower, often delayed when dealing with video and image formats.	The process is fast, as data is loaded almost immediately.
Adoption Stage	The method has been widely used for several decades, and has mature, well-tested tools.	Significantly newer approach with regular updates and less mature tools.
Extra Spends	Involves the services of a skilled IT specialist, which leads to extra spending on setup and maintenance.	Can be used by any team member with no advanced skills, and doesn't need regular service.
Data Volume	Suitable for smaller, well-structured warehouses.	Applicable for large data volumes.
Data Type	Structured data, limited formats.	Any sort of data and formats.
Data Lake	Incompatible.	Compatible.

Benefits and Drawbacks of ETL

While ETL may appear to be an outdated method, it's still adopted by many companies as it has some important advantages that are valued by many businesses.

ETL Benefits

• Ensures clean, well-structured data before loading, which results in more accurate analysis.
• Better suited for regulatory compliance like GDPR or HIPAA.
• Reliable, checked, and mature tools that are familiar to most IT workers.

ETL Drawbacks

• Limited list of formats for dealing.
• Slower operations and higher risks of improper file formatting.
• Bottlenecks and delays in work, when the amount of data increases unexpectedly.

Benefits and Drawbacks of ELT

As ELT continues to gain popularity, businesses are discovering both its strengths and its limitations.

ELT Benefits

• Decreased time needed for loading and transforming big files.
• Compliance with modern cloud-based tools and applications.
• Accessibility for all team members.

ELT Drawbacks: 

• May lead to violating compliance standards when used improperly.
• Less mature and reliable tools.

Conclusion

Making a choice between ETL and ELT depends on your business goals, capacity, and values. ETL can be an ideal solution for smaller and structured databases. It is commonly adopted by companies with stable IT support. In contrast, ELT should be chosen for companies with an active data flow and a strong focus on real-time monitoring. It's important to align the data integration method with your goals to make the right choice for your winning decisions and business growth.