3/10/2024 0 Comments Extract load transform tutorial![]() ![]() ![]() For example, because it transforms data before moving it to the central repository, ETL can make data privacy compliance simpler, or more systematic, than ELT (e.g., If analysts don’t transform sensitive data before they need to use it, it could sit unmasked in the data lake). There are other differences between ETL and ELT, too. They can support business intelligence, but more often, they’re created to support artificial intelligence, machine learning, predictive analytics and applications driven by real-time data and event streams. In ELT, the target data store can be a data warehouse, but more often it is a data lake, which is a large central store designed to hold both structured and unstructured data at massive scale.ĭata lakes are managed using a big data platform (such as Apache Hadoop) or a distributed NoSQL data management system. However, the order of steps is not the only difference. ELT does not transform any data in transit. ELT copies or exports the data from the source locations, but instead of moving it to a staging area for transformation, it loads the raw data directly to the target data store, where it can be transformed as needed. The obvious difference is the ELT process performs the Load function before the Transform function – a reversal of the second and third steps of the ETL process. Traditional ETL tools were designed to create data warehousing in support of Business Intelligence (BI) and Artificial Intelligence (AI) applications. It is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. However, there are several distinct differences between ELT and ETL, which stands for extract, transform and load. It’s possible to confuse ELT with its sister process known by a nearly identical acronym. Formatting the data into tables or joined tables based on the schema deployed in the warehouse.Removing, encrypting, hiding, or otherwise protecting data governed by government or industry regulations. ![]() This may include everything from changing row and column headers for consistency to converting currencies or units of measurement as well as editing text strings and adding or averaging values-whatever is needed to suit the organization’s specific BI or analytical purposes.
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