One of the most significant challenges for data-driven companies is getting their digital data in a clean centralized format. Nearly all businesses need information to determine what the most profitable decisions are. This information is usually buried in tons of data. The process of deriving this information is unknown to many high-level executives who know what information they want but aren't as familiar with the data acquisition process. This process is called ETL.
ETL stands for Extract Transform Load. In short, it's the process of taking data from one source (extract), modifying it to meet your preferences (transform), and uploading it into your preferred database (load). Without an efficient way to derive the needed data from all the disparate data sources, all the data in the world will mean nothing to the end-user.
The ETL process can be done manually, but when you take a moment and think of the number of data sources within an individual company's digital landscape, you can quickly come to understand how painstaking the time and effort of a manual ETL process will require. The solution, ETL tools.
An ETL tool automates the ETL process by offering three vital functions:
There are multiple ETL tools, but not all are built for the modern data environment.
Businesses require cost-efficient tools that are relatively easy to set up and use and support a wide range of use cases. Some of the ETL tools used throughout the digital data landscape today include:
There are several available ETL tools to choose from; Some of the factors to consider when choosing an ETL tool include: