Paul Houghton - Data Engineering with Alteryx: Helping data engineers apply DataOps practices with Alteryx

Data analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Alteryx for data engineers will show you how to create a data pipeline and apply DataOps principles to the management of Alteryx data projects.

Using real-world datasets, you will learn how to use the Alteryx Designer to reshape, clean, and aggregate your data. Then, you will learn how to apply the DataOps principles to automate and monitor the workflows you have created, thereby ensuring the creation of the best possible datasets.

If you're a data engineer, data scientist, or data analyst who wants 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 useful but is not necessary.

, Getting Started with Alteryx, introduces the Alteryx software suite and why you should use it as part of your data engineering processes.

, Data Engineering with Alteryx, focuses more on the specific application of Alteryx in a data engineering context. We understand the benefits of Alteryx for a data engineer and how to get started with Alteryx products.

, DataOps and Its Benefits, describes the DataOps process and why it is a good framework for data projects. It explores the principles for creating a good data product and how it can create high-performing data teams. We also explore how DataOps fits with the Alteryx products and how to leverage the principles when developing an Alteryx workflow.

, Sourcing the Data, explores the methods for extracting data with Alteryx. We look at the methods for connecting to local files and SQL databases in addition to the methods for extracting cloud-based data with application programming interfaces.

, Data Processing and Transformations, takes an example dataset from the previous chapter and describes common transformations required to process a raw dataset into an analytic resource for an organization.

, Sourcing the Data, and focuses on how to persist the dataset for future use. It examines the benefits of the saving methods and how each can be used for different applications.

, Extracting Value, introduces the methods for extracting insights and information from a dataset. We explore the methods for exploratory data analysis in Alteryx so that we can understand our dataset and gain organizational value from our data resources.

, Extracting Value, into the areas of spatial analytics and machine learning. We explore how to extract the geographic insights in our dataset using spatial tools. We also explore how to build a machine learning project in Alteryx using the predictive tools and the Intelligence Suite add-on.

, Testing Workflows and Outputs, describes how to use the message tool and the test tool to integrate testing processes and validation into our data pipeline. These checks improve the robustness of our dataset and provide early warning systems for data drift or data structure changes.

, Monitoring DataOps and Managing Changes, describes how to deploy continuous integration principles to an Alteryx pipeline. It allows for version and change management processes and confidence in dataset quality.

, Securing and Managing Access, introduces the best practices for managing an Alteryx server environment. We will learn how to manage access to workflows published to Alteryx Server and how to manage the infrastructure Alteryx Server is deployed on.

, Making Data Easy to Use and Discoverable with Alteryx, describes how Alteryx Connect can be used as a central data dictionary to help break the information silos in your organization and allow for the reuse of datasets across an organization.