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 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.
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. For more ETL Testing Certification
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.
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.
ETL and Traditional Uses
ETL is a proven method that many organizations rely on every day – such as retailers who need to see sales data regularly, or health care providers looking for an accurate depiction of claims. ETL can combine and surface transaction data from a warehouse or other data store so that it’s ready for business people to view in a format they can understand. ETL is also used to migrate data from legacy systems to modern systems with different data formats. It’s often used to consolidate data from business mergers, and to collect and join data from external suppliers or partners.
ETL With Big Data – Transformations and Adapters
Whoever gets the most data, wins. While that’s not necessarily true, having easy access to a broad scope of data can give businesses a competitive edge. Today, businesses need access to all sorts of big data – from videos, social media, the Internet of Things (IoT), server logs, spatial data, open or crowdsourced data, and more. ETL vendors frequently add new transformations to their tools to support these emerging requirements and new data sources. Adapters give access to a huge variety of data sources, and data integration tools interact with these adapters to extract and load data efficiently.
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