ETL is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. It’s often used to build a data warehouse. During this process, data is taken (extracted) from a source system, converted (transformed) into a format that can be analyzed, and stored (loaded) into a data warehouse or other system. Extract, load, transform (ELT) is an alternate but related approach designed to push processing down to the database for improved performance.
ETL History
ETL gained popularity in the 1970s when organizations began using multiple data repositories, or databases, to store different types of business information. The need to integrate data that was spread across these databases grew quickly. ETL became the standard method for taking data from disparate sources and transforming it before loading it to a target source, or destination. For more info ETL Testing online Training
In the late 1980s and early 1990s, data warehouses came onto the scene. A distinct type of database, data warehouses provided integrated access to data from multiple systems – mainframe computers, minicomputers, personal computers and spreadsheets. But different departments often chose different ETL tools to use with different data warehouses. Coupled with mergers and acquisitions, many organizations wound up with several different ETL solutions that were not integrated.
Over time, the number of data formats, sources and systems has expanded tremendously. Extract, transform, load is now just one of several methods organizations use to collect, import and process data. ETL and ELT are both important parts of an organization’s broader data integration strategy.
Why ETL Is Important
Businesses have relied on the ETL process for many years to get a consolidated view of the data that drives better business decisions. Today, this method of integrating data from multiple systems and sources is still a core component of an organization’s data integration toolbox. Learn ETL Testing Course
ETL is used to move and transform data from many different sources and load it into various targets, like Hadoop.
- When used with an enterprise data warehouse (data at rest), ETL provides deep historical context for the business.
- By providing a consolidated view, ETL makes it easier for business users to analyze and report on data relevant to their initiatives.
- ETL can improve data professionals’ productivity because it codifies and reuses processes that move data without requiring technical skills to write code or scripts.
- ETL has evolved over time to support emerging integration requirements for things like streaming data.
- Organizations need both ETL and ELT to bring data together, maintain accuracy and provide the auditing typically required for data warehousing, reporting and analytics.
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