Over the past few years, data wrangling (also known as data preparation) has emerged as a fast-growing space within the analytics industry. Once an analysis bottleneck due to painful, time-consuming work preparing diverse data sources for reporting and analysis, data wrangling technologies have come a long way.
As head of products at Trifacta (a data wrangling software vendor), one of the questions I repeatedly get asked in meetings with clients, partners, and analysts is, “What’s the difference between data wrangling and ETL?” Given how the features of the two technology spaces overlap in functionality, it’s a natural question to ask and one that the market needs to more clearly define.
To give you a clear understanding of the delineation between data wrangling and ETL, I’ll describe the top three major differences between the two technologies.
1. The Users Are Different The core idea of data wrangling technologies is that the people who know the data best should be exploring and preparing that data. This means Business analysts, line-of-Business users, and managers (among others) are the intended users of data wrangling tools. I can personally attest to the painstaking amount of design and engineering effort that has gone into developing a product that enables business people to intuitively do this work themselves.
In comparison, ETL technologies are focused on IT as the end users. IT employees receive requirements from their business counterparts and implement pipelines or workflows using ETL tools to deliver the desired data to the systems in the required formats.
Business users rarely see or leverage ETL technologies when working with data. Before data wrangling tools were available, these users’ interactions with data would only occur in spreadsheets or business intelligence tools.
2. The Data Is Different The rise of data wrangling software solutions came out of necessity.
