SQL Server 2019 Revealed

Including Big Data Clusters and Machine Learning

Bob Ward

PDF
ca. 46,99
Amazon iTunes Thalia.de Weltbild.de Hugendubel Bücher.de ebook.de kobo Osiander Google Books Barnes&Noble bol.com Legimi yourbook.shop Kulturkaufhaus ebooks-center.de
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.

Apress img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik

Beschreibung

Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology.

SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a “learn by example” approach for Intelligent Performance, security, mission-criticalavailability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters.

The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications.


What You Will Learn
  • Implement Big Data Clusters with SQL Server, Spark, and HDFS
  • Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sources
  • Combine SQL and Spark to build a machine learning platform for AI applications
  • Boost your performance with no application changes using Intelligent Performance
  • Increase security of your SQL Server through Secure Enclaves and Data Classification
  • Maximize database uptime through online indexing and Accelerated Database Recovery
  • Build new modern applications with Graph, ML Services, and T-SQL Extensibility with Java
  • Improve your ability to deploy SQL Server on Linux
  • Gain in-depth knowledge to run SQL Server with containers and Kubernetes
  • Know all the new database engine features for performance, usability, and diagnostics
  • Use the latest tools and methods to migrate your database to SQL Server 2019
  • Apply your knowledge of SQL Server 2019 to Azure


Who This Book Is For

IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability. 

Weitere Titel in dieser Kategorie

Kundenbewertungen

Schlagwörter

Intelligent Query Processing, Spark, Kubernetes, Java, SQL Server 2019, Artificial Intelligence (AI), Azure IOT Edge, HDFS, Containers, Big Data Clusters, Hadoop, Azure Data Studio, Linux