Analytics Optimization Columnstore Indexes by Pollack Edward (15 results)

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Paperback or Softback. Condition: New. Analytics Optimization with Columnstore Indexes in Microsoft SQL Server: Optimizing OLAP Workloads 1.16. Book.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Meet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels.With little or no knowledge of columnstore indexing you can become proficient with columnstore indexes as used in SQL Server, and apply that knowledge in development, test, and production environments. This book serves as a comprehensive guide to the use of columnstore indexes and provides definitive guidelines. You will learn when columnstore indexes shouldbe used, and the performance gains that you can expect. You will also become familiar with best practices around architecture, implementation, and maintenance. Finally, you will know the limitations and common pitfalls to be aware of and avoid.As analytic data can become quite large, the expense to manage it or migrate it can be high. This book shows that columnstore indexing represents an effective storage solution that saves time, money, and improves performance for any applications that use it. You will see that columnstore indexes are an effective performance solution that is included in all versions of SQL Server, with no additional costs or licensing required.What You Will LearnImplement columnstore indexes in SQL ServerKnow best practices for the use and maintenance of analytic data in SQL ServerUse metadata to fully understand the size and shape of data stored in columnstore indexesEmploy optimal ways to load, maintain, and delete data from large analytic tablesKnow how columnstore compression saves storage, memory, and timeUnderstand when a columnstore index should be used instead of a rowstore indexBe familiar with advanced features and analyticsWho This Book Is ForDatabase developers, administrators, and architects who are responsible for analytic data, especially for those working with very large data sets who are looking for new ways to achieve high performance in their queries, and those with immediate or future challenges to analytic data and query performance who want a methodical and effective solution 300 pp. Englisch.

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Meet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 300 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Meet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels.With little or no knowledge of columnstore indexing you can become proficient with columnstore indexes as used in SQL Server, and apply that knowledge in development, test, and production environments. This book serves as a comprehensive guide to the use of columnstore indexes and provides definitive guidelines. You will learn when columnstore indexes shouldbe used, and the performance gains that you can expect. You will also become familiar with best practices around architecture, implementation, and maintenance. Finally, you will know the limitations and common pitfalls to be aware of and avoid.As analytic data can become quite large, the expense to manage it or migrate it can be high. This book shows that columnstore indexing represents an effective storage solution that saves time, money, and improves performance for any applications that use it. You will see that columnstore indexes are an effective performance solution that is included in all versions of SQL Server, with no additional costs or licensing required.What You Will LearnImplement columnstore indexes in SQL ServerKnow best practices for the use and maintenance of analytic data in SQL ServerUse metadata to fully understand the size and shape of data stored in columnstore indexesEmploy optimal ways to load, maintain, and delete data from large analytic tablesKnow how columnstore compression saves storage, memory, and timeUnderstand when a columnstore index should be used instead of a rowstore indexBe familiar with advanced features and analyticsWho This Book Is ForDatabase developers, administrators, and architects who are responsible for analytic data, especially for those working with very large data sets who are looking for new ways to achieve high performance in their queries, and those with immediate or future challenges to analytic data and query performance who want a methodical and effective solution.