Dive into 'Data Engineering with Alteryx' to master the principles of DataOps while learning to build robust data pipelines using Alteryx. This book guides you through key practices to enhance data pipeline reliability, efficiency, and accessibility, making it an essential resource for modern data professionals. What this Book will help me do Understand and implement DataOps practices within Alteryx workflows. Design and develop data pipelines with Alteryx Designer for efficient data processing. Learn to manage and publish pipelines using Alteryx Server and Alteryx Connect. Gain advanced skills in Alteryx for handling spatial analytics and machine learning. Master techniques to monitor, secure, and optimize data workflows and access. Author(s) Paul Houghton is an experienced data engineer and author specializing in data engineering and DataOps. With extensive experience using Alteryx tools and workflows, Paul has a passion for teaching and sharing his knowledge through clear and practical guidance. His hands-on approach ensures readers successfully navigate and apply technical concepts to real-world projects. Who is it for? This book is ideal for data engineers, data scientists, and data analysts aiming to build reliable data pipelines with Alteryx. You do not need prior experience with Alteryx, but familiarity with data workflows will enhance your learning experience. If you're focused on aligning with DataOps methodologies, this book is tailored for you.

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics