ETL is an acronym for Extract, Transform, and Load. It's a process that takes data from an original source and puts it somewhere else in a format and structure that is more useful for the task at hand. ETL can be used to integrate data from multiple systems, clean it up, standardize it, and then put it into a new system ready to use and digest within an organization.
ETL is a part of Data Management. ETL stands for Extract, Transform and Load. These are 3 database functions combined into one tool to pull data out of one database and place it into another database.
Extract, Transform, and Load (ETL) is a three-step process used to transfer data from one location to another.
A data movement job is into three steps: Extract, Transform, and Load.
The Extraction phase involves gathering the required data from different sources. For example, the data might be extracted from a database or from a spreadsheet. The Extraction phase must be able to handle data in any format.
The Transformation phase converts the extracted data into a form suitable for data analysis. Data transformation includes cleaning of data, removing of unnecessary parts of the data such as headers and footers, and splitting columns into multiple columns or rows. It also includes filtering out records that are not needed or that do not conform to requirements and formatting information dictated by a data team so that downstream systems can handle it correctly. The transformed data is stored in a staging area where it can be used later for further analysis. A staging area is typically a set of tables in a database used for temporarily holding the results of transformations before they are loaded into their final destination.
The Load phase inserts the transformed data into its final target database. The target database can be an operational database stored in rows and columns.
ETL systems are generally applied to solve problems such as:
- Loading data warehouses or data marts
- Migrating data between different platforms (such as databases)
- Aggregating information from multiple locations
- Moving files for archival purposes
ETL has been around since the 1960s and is still widely used as an efficient process for data migration. Organizations use ETL tools to integrate their applications with existing systems and databases in a scalable, secure manner. The first use of the ETL paradigm was in the 1970s at IBM. The concept was introduced as a way to integrate disparate systems and databases so they could be analyzed collectively. It soon became clear that this was an effective technique for organizations to handle growth in data volume and complexity.
The growing volume of data produced by companies has led to a renewed interest in ETL tools. Today, there are many different types of ETL tools available on the market, each with their own sets of features and capabilities.
ETL tools are used in data warehousing. In this process, data from disparate sources is extracted, transformed into a standard format and loaded into a single centralized repository. The ETL process helps ensure accurate data storage, efficient use of resources, reduced redundancy and complete visibility of business intelligence across the enterprise.
Data warehousing is used for reporting and analysis. The end goal is to improve business processes by providing historical context to data analysis, as well as consistent information for reporting processes.