Definition of ETL vs ELT
What does is it mean and how is it used
The difference between ETL vs ELT?
Extract, Transform and Load, or ETL, is the traditional way of loading data into a data warehouse, where the data is copied to a staging area, transformed into the correct format and loaded into the warehouse.
Extract, Load and Transform, or ELT, is a different methodology where instead of transforming the data before it’s written, it is transformed in place in the target system. This leverages the power of the target data engine or appliance and reduces load times.
When to use ETL vs ELT?
It’s all about the way you like to load your data. ETL is fine if you only have a limited amount of data and you’re happy to load it yourself. But you might prefer ELT if you want bigger volumes of data loaded faster by a powerful data analytics engine.
Latest ETL/ELT Insights
In-DB tools avoid unnecessary data transfers between the database and Alteryx and hence provide a fast engine for ELT to speed up the data preparation process and to leave more time and resources for the value-added analysis and visualization steps.
After defining the business applications that will benefit from the enhanced performances of your new in-memory analytic database system, specific criteria should be set for the database and a complete set of benchmark requirements should be defined.
Many data warehousing solutions are readily available on AWS and it is often difficult to know which solution is better suited to your needs. A number of factors of AWS Redshift and Exasol were compared in our white paper, including speed, cost, concurrency, load times, and support.
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