Extract, Transform, and Load, or ETL, is the term used to refer to processes that data engineers use to collect data from a variety of sources, transform the data into a normalized form into a trusted resource. The resulting data is streamed into the other systems in which users can access it, use it downstream to solve business problems, and perform analytics. ETL can also be understood as the general procedure of moving data from one or more sources into a destination system. These target parts represent data from sources that have been normalized or that are an aggregation of several sources. The ETL process became a popular concept in the 1970s and is often used in data warehousing, data lakes, and data lake houses.

ETL is the data pipeline process to extract, transform, and load data from a source system into a target system such as a cloud data warehouse.

The Steps of ETL

  • Extraction: It involves collecting data from the source system.
  • Loading: During this step, the pipeline copies data from the source into the target system, usually a data warehouse or data lake.
  • Transformation: Once the data has been collected and loaded in the target system, it is ready to be transformed as needed. Often organizations will transform raw data in many ways to be used in other tools or business processes.

A well-designed ETL pipeline will collect data from source systems, applying data quality checks and consistency standards. The pipeline will then conform the data so that separate sources can be used together. Finally, the channel delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions.

Since data extraction can take time, it is common to run all three phases in the pipeline. While the data is being extracted, transformation processes are executed parallel on the data already received and prepared for loading. In contrast, the data loading begins without waiting for the previous phases to finish.

It is common for ETL systems to integrate data from multiple application systems, usually developed and supported by different cloud providers. These systems contain the original data and are frequently managed and operated by other users. For example, a transactional cost accounting system can combine payroll, sales, and purchasing data.

ETL is not the only paradigm for data processing. There is another version called ELT, which in principle is the same but with a different order of steps. ELT copies or exports the data from the source locations. Before loading it into a staging area for transformation, it loads the raw data directly into the target datastore to transform as needed.


Although both processes take advantage of various data repositories, such as databases, data warehouses, and data lakes, each approach has advantages and disadvantages depending on the context. ELT is beneficial for a large volume of unstructured data, as the loading can occur directly from the source. ELT may be ideal for managing big data, as you don’t need a lot of upfront planning for data extraction and storage. The ETL process, on the other hand, requires a more comprehensive definition upfront. You must identify the specific data needed for extraction and any potential sets to integrate into disparate source systems. Even after this, the data team must build the business rules for the data transformations. This job can generally have dependencies on the data requirements for a particular type of data analysis, which will determine the level of summary the data should have. Even though ELT has become increasingly popular with the adoption of cloud databases, it has its drawbacks for being the newest process, which means that best practices are still being established.

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