Microsoft Sql Server 2014 Query Tuning & Optimization (DATABASE & ERP - OMG)
Language: English
Published by McGraw-Hill Education, 2014
- Softcover
- Used

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Microsoft Sql Server 2014 Query Tuning & Optimization (DATABASE & ERP - OMG) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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- Title
- Microsoft Sql Server 2014 Query Tuning & Optimization (DATABASE & ERP - OMG)
- Author
- Benjamin Nevarez
- Publisher
- McGraw-Hill Education
- Publication year
- 2014
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0071829423
- ISBN 13
- 9780071829427
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.
Optimize Microsoft SQL Server 2014 queries and applications
Microsoft SQL Server 2014 Query Tuning & Optimization is filled with ready-to-use techniques for creating high-performance queries and applications. The book describes the inner workings of the query processor so you can write better queries and provide the query processor with the quality information it needs to produce efficient execution plans. You’ll also get tips for troubleshooting underperforming queries. In-Memory OLTP (Hekaton), a key new feature of SQL Server 2014, is fully covered in this practical guide.
- Understand how the query optimizer works
- Troubleshoot queries using extended events, SQL trace, dynamic management views (DMVs), the data collector, and other tools
- Work with query operators for data access, joins, aggregations, parallelism, and updates
- Speed up queries and dramatically improve application performance by creating the right indexes
- Understand statistics and how to detect and fix cardinality estimation errors
- Maximize OLTP query performance using In-Memory OLTP (Hekaton) features, including memory-optimized tables and natively compiled stored procedures
- Monitor and promote plan caching and reuse to improve application performance
- Improve the performance of data warehouse queries using columnstore indexes
- Handle query processor limitations with hints and other methods
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