Born out of necessity, data wrangling has emerged as a solution used to facilitate and expedite the data analysis process. With the constantly expanding amount of new and diverse data sources, business analysts can easily spend up to 80 percent of their time formatting and standardizing data before ever getting a chance to derive value from it. While traditional ETL technologies focus on enabling IT users to extract, transform and load data into a centralized enterprise data warehouse for reporting, data wrangling solutions are specifically designed for business users to explore and prepare diverse data themselves for a variety of downstream uses.
As head of products at Trifacta, clients, partners and analysts are always asking me to differentiate data wrangling and ETL. Given there is some overlap in functionality across the two tool sets, I can understand why there is some confusion. To highlight the differences between data wrangling and ETL, I’ll explain three major differences between the two technologies.
Check out the full article on TDWI here.