ETL vs ELT - What’s The Difference?

ETL and ELT are two common approaches in data integration, each designed to transfer data from one place to another while catering to different data needs.

ETL, which stands for Extract, Transform, and Load, involves transforming data on a separate processing server before transferring it to the data warehouse.

ELT, on the other hand, or Extract, Load, and Transform, performs data transformations directly within the data warehouse itself. Unlike ETL, ELT allows raw data to be sent directly to the data warehouse, eliminating the need for staging processes.

The main difference between both is that ETL transforms data before loading it on the server, while ELT transforms it afterward.

ETL is an older method ideal for complex transformations of smaller data sets and is great for those prioritizing data security. Conversely, ELT is a newer technology that provides more flexibility to analysts and is perfect for processing both structured and unstructured data.

Your decision between ETL and ELT will determine your data storage, analysis, and processing. So, before choosing between the two methods, it’s important to consider all factors, including the type of business you are running and your data needs.

Read on to discover everything you need to choose the right data integration method for your business.

What is ETL (Extract, Transform, Load)?

Extract, transform, and load (ETL) is a data integration methodology that extracts raw data from sources, transforms the data on a secondary processing server, and then loads it into a target database.

ETL is used when data must be transformed to conform to the data regime of a target database. The method emerged in the 1970s and remains prevalent among on-premise databases that possess finite memory and processing power.

Consider an example of ETL in action. Online Analytical Processing (OLAP) data warehouses only accept relational SQL-based data structures. In this scenario, a protocol such as ETL ensures compliance by routing the extracted data to a processing server and then transforming the non-conforming data into SQL-based data. The extracted data only moves from the processing server to the data warehouse once it has been successfully transformed.

What Is ELT (Extract, Load, Transform)?

Unlike ETL, extract, load, and transform (ELT) does not require data transformations to take place before the loading process.

ELT loads raw data directly into a target data warehouse instead of moving it to a processing server for transformation.

With ELT, data cleansing, enrichment, and transformation all occur inside the data warehouse itself. Raw data is stored indefinitely in the data warehouse, allowing for multiple transformations.

ELT is a relatively new development made possible by the invention of scalable cloud-based data warehouses. Cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure possess the digital infrastructure, in terms of storage and processing power, to facilitate raw data repositories and in-app transformations.

Although ELT is not universally used, the method is gaining popularity as companies adopt cloud infrastructure.

ETL vs ELT: How is ETL Different from the ELT Process?

ETL and ELT differ in two primary ways:

1. Transformation Location: ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.
2. Data Retention: ETL does not transfer raw data into the data warehouse, while ELT sends raw data directly to the data warehouse.

ELT processes data faster than ETL. ETL includes a preliminary transformation step before loading data into the target, which becomes challenging to scale and slows performance as data size grows. ELT, in contrast, loads data directly into the target system, transforming it in parallel.

The raw data retention of ELT creates a rich historical archive for generating business intelligence. As goals and strategies change, BI teams can re-query raw data to develop new transformations using comprehensive datasets. ETL, on the other hand, does not generate complete raw data sets that are endlessly queryable.

These factors make ELT more flexible, efficient, and scalable, especially for ingesting large amounts of data, processing datasets that contain both structured and unstructured data, and developing diverse business intelligence.

A Brief History: ETL & ELT Processes

Today, businesses across the spectrum require data integration to centralize, access, and activate data across their organizations. The challenge of combining multiple data sources into a unified view has been a long-standing issue since the dawn of the digital era.

ETL emerged in the 1970s as the first standardized method for facilitating data integration. It became essential as businesses adopted multi-pronged computer systems and heterogeneous data sources. With the rise of data warehouses in the 1980s, ETL became increasingly important.

The emergence of cloud computing in the 2000s paved the way for ELT, enabling businesses to load unlimited raw data straight into a cloud data warehouse, unlocking the analytical potential of big data.

Which is Better: ETL or ELT?

If you want to transform your data before loading it into your data warehouse or data lake, then ETL might be the right choice. If you are dealing with large datasets or need to keep raw data available for future analysis, then ELT might be the best approach.

Ultimately, it comes down to the specific data sets you are working with and how you design your data pipelines.

By aligning your data strategy with the right integration method, you can unlock the full potential of your data and drive actionable insights for your business.

Ready to Elevate Your Data Strategy?

At DataTide, we specialize in optimizing data integration processes to help you leverage the right methodologies for your business needs. Contact us today to discover how we can assist you in navigating the complexities of ETL and ELT, ensuring your data is effectively harnessed for maximum value!