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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    ISBN: 9781484259856
    Language: English
    Pages: 1 online resource (272 pages)
    Edition: 2nd edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Use this guide to one of SQL Server 2019's most impactful features-Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL-taking advantage of skills you have honed for years-and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments
    Note: Online resource; Title from title page (viewed May 23, 2020) , Mode of access: World Wide Web.
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    ISBN: 9781484280850 , 1484280857
    Language: English
    Pages: 1 online resource
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Microsoft Azure (Computing platform) ; Cloud computing ; Infonuagique ; Cloud computing ; Microsoft Azure (Computing platform) ; Electronic books
    Abstract: Get introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more. This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can't move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports. What You Will Learn Understand the fundamentals and architecture of Azure Arc-enabled data services Build a multi-cloud strategy based on Azure Data Services Deploy Azure Arc-enabled data services on premises or in any cloud Deploy Azure Arc-enabled SQL Managed Instance on premises or in any cloud Deploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloud Backup and Restore your data that is managed by Azure Arc-enabled data services Manage Azure-enabled data services running outside of Azure Monitor Azure-enabled data services through Grafana and Kibana Monitor Azure-enabled data services running outside of Azure through Azure Monitor.
    Note: Includes index. - Online resource; title from PDF title page (SpringerLink, viewed March 7, 2022)
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    ISBN: 9781484251102
    Language: English
    Pages: 1 online resource (255 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Get a head-start on learning one of SQL Server 2019's latest and most impactful features-Big Data Clusters-that combines large volumes of non-relational data for analysis along with data stored relationally inside a SQL Server database. This book provides a first look at Big Data Clusters based upon SQL Server 2019 Release Candidate 1. Start now and get a jump on your competition in learning this important new feature. Big Data Clusters is a feature set covering data virtualization, distributed computing, and relational databases and provides a complete AI platform across the entire cluster environment. This book shows you how to deploy, manage, and use Big Data Clusters. For example, you will learn how to combine data stored on the HDFS file system together with data stored inside the SQL Server instances that make up the Big Data Cluster. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019 using Release Candidate 1. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL-taking advantage of skills you have honed for years-and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For For data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environment
    Note: Online resource; Title from title page (viewed November 26, 2019)
    Library Location Call Number Volume/Issue/Year Availability
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