Data Engineering With Alteryx Helping Data Engineers Apply Dataops Practices With Alteryx Houghton by Houghton, Paul 9781803236483, 1803236485 instant download after payment.

Build and deploy data pipelines with Alteryx by applying practical DataOps principles Key Features: Learn DataOps principles to build data pipelines with Alteryx Build robust data pipelines with Alteryx Designer Use Alteryx Server and Alteryx Connect to share and deploy your data pipelines Book Description: 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. What You Will Learn: 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 Who this book is for: 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 Da