ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.One difference is where the data is transformed, and the other difference is how data warehouses retain data. Raw data is stored indefinitely in the data warehouse, allowing for multiple transformations.ĮLT is a relatively new development, made possible by the invention of scalable cloud-based data warehouses.Ĭloud 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.Īlthough ELT data pipeline is not used universally, the method is becoming more popular as companies adopt cloud infrastructure.ĮTL vs ELT: How is ETL Different from the ELT Process?ĮTL and ELT differ in two primary ways. With ELT data pipeline, data cleansing, enrichment, and data transformation all occur inside the data warehouse itself.

Unlike ETL, extract, load, and transform (ELT) does not require data transformations to take place before the loading process.ĮLT loads raw data directly into a target data warehouse, instead of moving it to a processing server for transformation. The extracted data only moves from the processing server to the data warehouse once it has been successfully transformed. 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. Online Analytical Processing (OLAP) data warehouses only accept relational SQL-based data structures. The method emerged in the 1970s, and remains prevalent amongst on-premise databases that possess finite memory and processing power.Ĭonsider an example of ETL in action.

Read on to discover everything you need to choose the right data integration method for your business.Įxtract, 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.ĮTL is used when data must be transformed to conform to the data regime of a target database. This includes the type of business you are running and your data needs.

So, before choosing between the two methods, it’s important to consider all factors. Your decision between ETL and ELT will determine your data storage, analysis, and processing. On the other hand, ELT is a newer technology that provides more flexibility to analysts and is perfect for processing both structured and unstructured data. It’s also great for those prioritizing data security. Their most important difference is that ETL transforms data before loading it on the server, while ELT transforms it afterward.ĮTL is an older method ideal for complex transformations of smaller data sets. However, each has unique characteristics and is suitable for different data needs. Their main task is to transfer data from one place to another.

Unlike ETL, ELT allows for raw data to be sent directly to the data warehouse, eliminating the need for staging processes.ĮTL and ELT are data integration methods. On the other hand, ELT, or Extract, Load, and Transform, performs data transformations directly within the data warehouse itself. What is the difference between ETL and ELT?ĮTL and ELT are two common approaches in data integration.ĮTL, which stands for Extract, Transform, and Load, involves transforming data on a separate processing server before transferring it to the data warehouse.

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