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What is the difference between ETL and ELT?
ETL stands for Extract, transform, and load. It is the process data-driven organizations use to gather data from multiple sources and then bring it together to support discovery, reporting, analysis, and decision-making.
It is tempting to think a creating a Data warehouse is simply extracting data from multiple sources and loading it into the database of a Data warehouse. The ETL process needed active inputs from multiple stakeholders including developers, analysts, testers, top administration, and is technically difficult.
It can support its value as a tool for decision-makers. Data warehouse system needs to change with business development. ETL is a constant activity of a Data warehouse system and is required to be rapid, computerized, and well authoritative.
The ETL tools for enterprise data warehouses should meet data integration requirements, including high-volume, high-execution batch loads; event-driven, trickle-feed integration procedure; programmable transformations; and orchestrations so they can deal with the most demanding transformations and workflows and have connectors for the most distinct data sources.
ETL has multiple use cases across several fields. One of them is deriving value from customer data and customers communicate with a brand in multiple ways. ETL collates all these customer data from several sources, transforms the information to observe to a standard format, and then loads it into a data warehouse or multiple data sources for analysis.
When the company can easily analyze their data that is all in the same language and the same location, this gives the organization an accurate 360-degree view of the customer's interaction with their brand. It enables the organization to understand customer needs and provide them with a highly personalized experience.
ELT stands for Extract, load, and transform. It is the phase of extracting information from different sources and loading it into a target data warehouse. ELT is an alternative to the traditional extract, transform and load (ETL) process. It pushes the transformation element of the process to the target database for better achievement. This facility is very beneficial for processing the massive data sets required for business intelligence (BI) and big data analytics.
Because it takes benefit of the processing capability already construct into a data storage infrastructure, ELT decreases the time data spends in transit and improves efficiency.
It can implement the data integrity analysis in the staging method enables a further phase in the process to be private and assigned with at the most appropriate point in the process. This method also helps to provide that only cleaned and checked data is loaded into the warehouse for transformation.
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