ETL is an acronym for Extract Transform Load. ETL is a type of software that collects data from multiple sources, converts it into a format suitable for a data warehouse and transfers it to the data warehouse. Find out everything you need to know about it.
ETL (Extract Transform Load) software allows raw data to be extracted from a database, then restructured and finally loaded into a Data Warehouse. This software has been around for a long time, but has evolved to meet the new needs of the Cloud, SaaS (Software as a Service) and Big Data.
ETLs must now enable real-time ingest, data enrichment, and support billions of transactions. They also support structured or unstructured data from on-site or cloud-based sources. Likewise, these platforms must now be scalable, flexible, fault tolerant, and secure.
ETL: What is it?
The first ETLs appeared in the 1970s. Lajor companies have begun to aggregate and store data of different types from multiple sources. These software programs were born out of the need to integrate this diverse data.
During the boom of data warehouses in the 1980sMost of the data warehouses were only compatible with a specific ETL. Companies were therefore forced to use a large number of them.
Over time, the number of sources and types of data increasedas well as the number of ETL vendors. This helped bring prices down until these solutions were available to most companies. Thus, these tools contributed to the emergence of data-driven companies.
ETL: How does it work?
To understand how ETL’s solutions work, let’s take a look at the example of a company that sells products both in a physical shop and on the web. This company needs to analyze all sales trends simultaneously.
However, the data collected online and in-store may not be in the same format. In addition, the data collection systems may not be able to communicate with each other. The role of ETL software is to collect the relevant data from both systems, transform it to make it compatible with the Data Warehouse, and finally load it into the Data Warehouse.
The operation of the ETL platform is divided into three phases. Lhe Extraction phase is to collect data from one or more sources.
The transformation phase consists of reformat and transform data. Finally, the loading phase consists of transferring the transformed data to the Data Warehouse, the Data Store or the target database.
ETL: What’s that for?
ETLs have multiple use cases. Their primary use is to transform data for transfer to Data Warehouses, but they can also be used to transfer data from legacy systems to modern systems with different data formats.
In the age of Big Data, the Internet of Things, social networks, videos or Open Data, ETLs are also adapting to new types and sources of data. Similarly, modern tools allow data to be transferred directly to the Hadoop platform. Some modern solutions also offer a self-service approachThe new system also includes data quality tools and metadata support.