PolyBase Revealed: Data Virtualization with SQL Server, Hadoop, Apache Spark, and Beyond
By 作者: Kevin Feasel
ISBN-10 书号: 1484254600
ISBN-13 书号: 9781484254608
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-12-21
pages 页数: (311 )
Book Description to Finelybook sorting
Harness the power of PolyBase data virtualization software to make data from a variety of sources easily accessible through SQL queries while using the T-SQL skills you already know and have mastered.
PolyBase Revealed shows you how to use the PolyBase feature of SQL Server 2019 to integrate SQL Server with Azure Blob Storage, Apache Hadoop, other SQL Server instances, Oracle, Cosmos DB, Apache Spark, and more. You will learn how PolyBase can help you reduce storage and other costs by avoiding the need for ETL processes that duplicate data in order to make it accessible from one source. PolyBase makes SQL Server into that one source, and T-SQL is your golden ticket. The book also covers PolyBase scale-out clusters, allowing you to distribute PolyBase queries among several SQL Server instances, thus improving performance.
With great flexibility comes great complexity, and this book shows you where to look when queries fail, complete with coverage of internals, troubleshooting techniques, and where to find more information on obscure cross-platform errors. Data virtualization is a key target for Microsoft with SQL Server 2019. This book will help you keep your skills current, remain relevant, and build new business and career opportunities around Microsoft’s product direction.
What You Will Learn
Install and configure PolyBase as a stand-alone service, or unlock its capabilities with a scale-out cluster
Understand how PolyBase interacts with outside data sources while presenting their data as regular SQL Server tables
Write queries combining data from SQL Server, Apache Hadoop, Oracle, Cosmos DB, Apache Spark, and more
Troubleshoot PolyBase queries using SQL Server Dynamic Management Views
Tune PolyBase queries using statistics and execution plans
Solve common business problems, including “cold storage” of infrequently accessed data and simplifying ETL jobs