Kevin Bartley

ETL and ELT are both data integration methods that transfer data from a source to a data warehouse. Despite similarities, ETL and ELT differ in fundamental ways. Here’s a quick comparison of ETL and ELT (ETL vs ELT).

What is ETL (Extract, Transform, Load)?

ETL vs ELT

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 the data 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 amongst 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.

With this kind of data warehouse, 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 all have the digital infrastructure, in terms of storage and processing power, to facilitate raw data repositories and in-app transformations.

Although ELT is not used universally, the method is becoming more popular as companies adopt cloud infrastructure.

ETL vs. ELT: What’s the Difference?

ETL and ELT differ in two primary ways. One difference is where the data is transformed, and the other difference is how data warehouses retain data.

  • ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.
  • ETL does not transfer raw data into the data warehouse, while ELT sends raw data directly to the data warehouse.

For ETL, the process of data ingestion is made slower by transforming data on a separate server before the loading process.

ELT, in contrast, delivers faster data ingestion, because data is not sent to a secondary server for restructuring. In fact, with ELT, data can be loaded and transformed simultaneously.

The raw data retention of ELT creates a rich historical archive for generating business intelligence. As goals and strategies change, BI teams can requery 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 data sets that contain both structured and unstructured data, and developing diverse business intelligence.

On the other hand, ETL is ideal for compute-intensive transformations, systems with legacy architectures, or data workflows that require manipulation before entering a target system, such as erasing personal identifying information (PII).

A Brief History of Data ETL & ELT

Today, businesses across the spectrum require data integration to centralize, access, and activate data across their organizations. Businesses must leverage dozens or hundreds of different data sources, across countries, continents, and teams, to drive results in the data-driven economy. In this complex, fractured landscape, combining multiple data sources into a unified view has never been more important. But this challenge is nothing new. Data integration has vexed organizations since the dawn of the digital era. 

The first inventions of the modern computer age laid the groundwork for the unique function of data integration. In the late 1960s, disk storage supplanted punch cards, enabling direct access to data. Not long after, IBM and other companies pioneered the first Database Management Systems (DBMS). These advancements soon led to the sharing of data between computers. Almost immediately, the cumbersome process of integrating data and data sources with external machines became a challenge.    

ETL, the first standardized method for facilitating data integration, emerged in the 1970s. ETL rose to prominence as enterprise businesses adopted multi-pronged computer systems and heterogeneous data sources. These businesses needed a way to aggregate and centralize data from transactions, payroll systems, inventory logs, and other enterprise resource planning (ERP) data.  

With the rise of data warehouses in the 1980s, ETL became even more essential. Data warehouses could integrate data from a variety of sources, but typically required custom ETLs for each data source. This led to an explosion of ETL tools. By the end of the 1990s, many of these solutions finally became affordable and scalable for mid-market businesses, not just large enterprises. 

With the emergence of cloud computing in the 2000s, cloud data lakes and data warehouses caused a new evolution: ELT. With ELT, businesses could load unlimited raw data straight into a cloud DWH. Engineers and analysts could execute an infinite number of SQL queries on top of this raw data, directly inside the cloud data warehouse itself. For the first time, businesses could unlock the analytical firepower and efficiency that big data had always promised. Combined with visualization tools and cloud DWHs, ELTs ushered in a new era of analytics and data-driven decision-making. 

ETL and ELT: The Best Fit Is Based On Each Team

Cloud data warehouses have given rise to a new frontier in data integration, but choosing between ETL and ELT depends on a team’s needs.

Although ELT offers exciting new advantages, some teams will remain with ETL because the method makes sense for their particular deployment, legacy infrastructure or not.

Whatever the choice, data teams across the spectrum are finding success actuating their integration strategies by harnessing a data integration platform.

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