Every company nowadays requires data to inform their business decisions. In order to have data be readily accessible and stored in one place, companies have to set up various data integration pipelines.
These are the main components within the data integration stack:
The traditional data integration architecture looks like this:
More recently, there has been a shift towards ELTs. With ELTs, the data integration architecture looks something like this:
Simplistically, transformations are any type of manipulation on the raw datasets to present a particular finding or view. Here are some examples of typical data transformations:
There are endless amounts of possible transforms. To get a better understanding of what additional transformations may look like, check out all the Snowflake functions !
Transformations are maintained by the team that works closer with the actual data
Transforms can be implemented in SQL or with dbt from the data warehouse, as opposed to custom scripts in the data pipeline. This means that business analysis, data scientists, and others will be able to write their own transforms and maintain the definition.
ELTs build upon a self-serve data culture and frees up engineering time
With ELTs, the data team can write their own transformations and not have to rely on the engineering team to write custom scripts.
More ways to analyze the datasets
With the ETL approach, the process only outputs and uploads the transformed data set and is extremely rigid. As a result of this, it prevents others from performing additional queries and masks the underlying tables that are referenced in the transformation.
Whereas with ELT, the raw data is loaded into the data warehouse and transformations are done within the data warehouse.
Artie Transfer helps companies adopt and supercharge ELT workflows by providing real-time data replication from transactional databases to your data warehouse.
Contact us if you're interested in learning more!
