Big Data Glossary
What is ETL?
Definition of ETL
What is ETL?
ETL stands for Extract, Transform, Load. It is a process used to move data from one or more sources into a destination system, such as a data warehouse or a data lake. The process is typically broken down into three stages:
Extract: Data is extracted from various sources, such as databases, files, or APIs. This can include structured data, such as relational databases, and unstructured data, such as log files or social media feeds.
Transform: The extracted data is then transformed, or cleaned and processed, to make it ready for loading into the destination system. This can include tasks such as filtering, sorting, and aggregating the data, as well as converting it into a format that can be loaded into the destination system.
Load: The transformed data is then loaded into the destination system, such as a data warehouse or a data lake.
ETL is commonly used in data warehousing and business intelligence, to integrate data from different sources and make it available for reporting and analysis. It can also be used in data integration projects, where data from different systems needs to be consolidated for reporting or other uses. Some popular ETL tools are Informatica, Talend, and DataStage.
ETL is a process that helps organizations to collect, integrate, and manage large and complex data sets from multiple sources. It is a critical part of data warehousing and business intelligence initiatives that allow organizations to make sense of their data and gain insights that can inform business decisions.
During the extraction phase, data is retrieved from various sources such as databases, flat files, web services, and other external systems. The extracted data is then cleaned, transformed and standardized to ensure that it is in a format that can be loaded into the destination system. This can include tasks such as removing duplicates, converting data types, applying calculations, and applying business rules.
The transformed data is then loaded into a destination system, such as a data warehouse or a data lake. This allows the data to be made available for reporting, analysis and decision-making. The destination system typically uses a data model that organizes the data in a way that makes it easy to access and query.
ETL processes are typically automated and run on a regular schedule, such as daily or weekly, to ensure that the data in the destination system is up-to-date. The process can also be triggered by specific events, such as the arrival of a new file or the completion of a transaction. With the rise of big data and IoT, ETL process are becoming more complex and sophisticated, with more powerful tools, and more advanced techniques, like machine learning and natural language processing, are being used to automate the process.
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