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What Are ETLs and Why Are They Important?
Creating a world of self-service analytics
The rise in self-service analytics is a significant selling point in the business intelligence world. Part of the point of creating self-service analytics is having easy access to the data from your organization.
The question is how do you get your data from external application data sources into a usable format?
The answer is ETLs.
These days, ETLs (Extract, Transform, Load) are a vital aspect of Business Intelligence (BI). With ETLs, data from different sources can be grouped into a single place for analytics programs to act on and realize key business insights. ETL is here and it is highly significant.
What is ETL (Extract, Transform, Load)?
Data is the foundation of the modern business world. Data on its own is not very useful. On top of that, the data is often stored in some form of application database that isn’t easy to use for analytics.
This is why ETL tools are essential. ETLs take data from multiple systems and combine them into a single database (often referred to as a data warehouse) for analytics or storage.
ETLs imply data migrating from one application/database to an analytical database. An ETL takes three steps to get the data from database A to database B. These are:
- Extract (E)
- Transform (T)
- Load (L)
Extract
The extract function involves the process of reading the data within a database. This stage also involves data collection and extraction. Depending on the type of system the extract might operate in several different ways. It could extract the data into some form of flat file or just directly pull it from an API. This is dependent on the risk of interacting with the application system, the timing requirements, and several other technical restrictions.
