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. ![]()
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