Cloud Data Integration – ETL vs ELT
The question of ETL versus ELT has been the topic of discussion lately. In this post, we’ll look at some of the features that are a good fit for modern cloud data warehouse and the challenges that underlie the two approaches.
Our previous blog posts presented details of agile analytics using Azure SQL Data Warehouse and Snowflake and the benefits of moving data into the cloud. Cloud analytics is becoming prominent with improved agility and scalability. We will look at how cloud data integration with ELT proves an important factor.
ETL is Getting Outdated
ETL has played a vital part for movement of data between various layers in a traditional on-premises data warehouse. It starts with a collection/extraction layer followed by the transformation layer for business rules and ends with a reporting layer built for analytics.
There are proven success stories on the ETL approach as it is more established and has been in the industry for decades. We can find best practices and plenty of how-to guides that act as a knowledge base. Below we will discuss how ELT differs and better suits modern cloud data warehouse solutions.
ELT is Emerging
With the swap of ‘T’ and ‘L’, ELT refers to extracting data, loading everything into target system and then applying any transformations after it is loaded. Based on the solution, data can be loaded into cloud data warehouse, a data lake, or both, thereby allowing users/data analysts to work as they choose.
ELT is replacing ETL and fits into cloud data integration processes due to the factors discussed above. ELT is a relatively new concept, shifting data preparation effort to the time of analytic use. ELT works well for both data warehouse modernization and supports data lake deployments. This enables customers to see a combination of data from centralized repositories as well as flexible BI reporting solutions based on their business needs.
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