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Data Engineering with Alteryx

Data Engineering with Alteryx

By : Paul Houghton
4.8 (11)
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Data Engineering with Alteryx

Data Engineering with Alteryx

4.8 (11)
By: Paul Houghton

Overview of this book

Alteryx is a GUI-based development platform for data analytic applications. Data Engineering with Alteryx will help you leverage Alteryx’s code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have. This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You’ll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you’ll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process. By the end of this Alteryx book, you’ll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources.
Table of Contents (18 chapters)
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1
Part 1: Introduction
5
Part 2: Functional Steps in DataOps
11
Part 3: Governance of DataOps

Our Alteryx data pipeline

The Alteryx data pipeline we created was an example of downloading place information from an authenticated API and using this as a dataset to enrich internal data records.

The data sourcing step from Chapter 4, Sourcing the Data, used the Google Maps Places API. We wanted a place location dataset to identify places that could impact our business. Following a DataOps process, we needed to quickly create a dataset for this future analysis and make the dataset robust enough to be used for other applications later.

Once we had a raw dataset, we needed to transform the download into a format from which our users could extract value. The data we downloaded from the API was in JavaScript Object Notation (JSON) format. This format works for many applications, but performing analytic processes in Alteryx required transformation into a more common tabular form. We followed a five-step process using many tools common to various analytical techniques. The five steps...

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