ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. This transformation could involve cleaning, aggregating, or summarizing the data. ELT is commonly used in big data projects and real-time processing where speed and scalability are critical.
In the past, data was often stored in a single location, such as a database or a data warehouse. However, with the rise of the internet and cloud computing, data is now generated and stored across multiple sources and platforms. This dispersed data environment creates a challenge for businesses that need to access and analyze their data. ELT offers a solution to this challenge by allowing companies to extract data from various sources, load it into a central location, and then transform it for analysis.
The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.
This is part of a series of articles about ETL
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