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Principles of Data Wrangling by Connor Carreras, Sean Kandel, Jeffrey Heer, Tye Rattenbury, Joseph M. Hellerstein

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Chapter 9. Data Wrangling Tools

Tools for data wrangling span a number of dimensions, from general-purpose programming languages, to commodity spreadsheet applications, to visual transformation and profiling products. There are easily dozens of tools in each category, but for the purposes of this book, we’re going to focus on three products that we believe represent the different focuses and strengths of each dimension of data wrangling tools: Excel, SQL, and Trifacta Wrangler. If you’ve worked with data in any capacity, you’re probably familiar with one or more of these tools.

As we move through this chapter, we will draw distinctions between these three tools based on their supported data size, required infrastructure, supported data structures, and transformation paradigms. We want to illuminate the generic use cases for which each tool fits so that you can understand which one would best suit a particular data project.

Let’s begin with a brief overview of each tool. The two most commonly used data wrangling tools are Excel and SQL. Both are more than 30 years old, which attests to the longevity of data wrangling as a task. Trifacta Wrangler, in contrast, was launched in 2012 as an outgrowth of academic research at UC Berkeley and Stanford. SQL is considered a general-purpose tool for data manipulation, and as such, is widely embedded in many relational database distributions. Excel is a spreadsheet application that allows users to manipulate, analyze, and store data in a tabular ...

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