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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

The benefits the DataOps framework brings to your organization

As a reminder, DataOps is the methodology that defines the systems and structures for building data pipelines. The most significant benefits of DataOps are as follows:

  • Faster cycle times
  • Faster access to actionable insights
  • Improved robustness of data processes
  • The ability to see the entire data flow in a workflow
  • Strong security and confidence

These benefits have allowed me to deliver data pipelines to my customers and end-users faster. The speed of delivery enables end users to analyze their datasets and immediately provide the feedback needed to tune that dataset to the result they need. Additionally, the inclusion of testing, reporting, and monitoring has provided confidence in the dataset when delivering the completed project for them to maintain in the future.

For example, when building an integration pipeline for a customer, returning incremental workflow improvements faster allows...

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