Code: USD75OFF
This is the code repository for Data Engineering with Alteryx, published by Packt.
Helping data engineers apply DataOps practices with Alteryx
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 covers the following exciting features:
- Build a working pipeline to integrate an external data source
- Develop monitoring processes for the pipeline example
- Understand and apply DataOps principles to an Alteryx data pipeline
- Gain skills for data engineering with the Alteryx software stack
- Work with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integration
- Organize content on Alteryx Server and secure user access
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
from os import listdir
import os
import xml.etree.ElementTree as ET
ayx_file = ('.yxmd', '.yxmc', '.yxwz')
files = [f for f in listdir(os.getcwd()) if f.endswith(ayx_file)]
Following is what you need for this book: If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You’ll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| 1-13 | Alteryx Designer | Windows |
| Alteryx Designer Intelligence Suite | ||
| Alteryx Server | ||
| Alteryx Connect |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Paul Houghton is an experienced business analyst with the ability to make focused data-led decisions. He is able to utilize data from a multitude of sources, including structured company data alongside unstructured data, such as social media sites. Paul's ability to combine data from structured business sources with open and unstructured data and analyze a range of datasets enables him to make fast, accurate, and relevant business decisions.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.