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  • 1
    Online Resource
    Online Resource
    Pro SQL Server 2019 Wait Statistics : A Practical Guide to Analyzing Performance in SQL Server (2019)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484249161
    Language: English
    Pages: 1 online resource (407 pages)
    Edition: 2nd edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Here is a practical guide for analyzing and troubleshooting SQL Server performance using wait statistics. Learn to identify precisely why your queries are running slowly. Measure the amount of time consumed by each bottleneck so that you can focus attention on making the largest improvements first. This edition is updated to cover analysis of wait statistics inside Query Store, the CXCONSUMER wait event, and to be current with SQL Server 2019. Whether you are new to wait statistics, or already familiar with them, this book provides a deeper understanding on how wait statistics are generated and what they can mean for your SQL Server instance’s performance. Pro SQL Server 2019 Wait Statistics goes beyond the most common wait types into the more complex and performance-threatening wait types. You’ll learn about per-query wait statistics and session-based wait statistics, and the types of problems they each can help you solve. The different wait types are categorized by their area of impact, including CPU, IO, Lock, and many more. The book presents clear examples to help you gain practical knowledge of why and how specific wait times increase or decrease, and how they impact your SQL Server’s performance. After reading this book you won’t want to be without the valuable information that wait statistics provide regarding where you should be spending your limited tuning time to maximize performance and value to your business. What You'll Learn Identify resource bottlenecks in a running SQL Server instance Locate wait statistics information inside DMVs and Query Store Analyze the root cause of sub-optimal performance Diagnose I/O contention and locking contention Benchmark SQL Server performance Lower the wait time of the most popular wait types Who This Book Is For Database administrators who want to identify and resolve performance bottlenecks, those who want to learn more about how the SQL Server engine accesses and uses resources inside SQL Server, and administrators concerned with achieving—and knowing they have achieved—optimal performance
    Note: Online resource; Title from title page (viewed June 3, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    SQL Server Big Data Clusters : Early First Edition Based on Release Candidate 1 (2019)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    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)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    SQL Server Big Data Clusters : Data Virtualization, Data Lake, and AI Platform (2020)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Pro SQL server wait statistics (2015)
    [New York, NY] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: The expert's voice in SQL server
    Keywords: SQL server ; Database management ; Client/server computing ; Electronic books ; Electronic books ; local
    Abstract: Pro SQL Server Wait Statistics is a practical guide for analyzing and troubleshooting SQL Server performance using wait statistics. Whether you are new to wait statistics, or already familiar with them, this book will help you gain a deeper understanding of how wait statistics are generated and what they can mean for your SQL Server's performance. Besides the most common wait types, Pro SQL Server Wait Statistics goes further into the more complex and performance threatening wait types. The different wait types are categorized by their area of impact, and include CPU, IO, Lock, and many more different wait type categories. Filled with clear examples, Pro SQL Server Wait Statistics helps you gain practical knowledge of why and how specific wait times increase or decrease, and how they impact your SQL Server's performance.
    Note: Place of publication from publisher's website. - Includes index. - Description based on online resource; title from title page (Safari, viewed September 17, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781484211397/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
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