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on1400015856 |
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OCoLC |
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20240909213021.0 |
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m o d |
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cr cnu|||unuuu |
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230928s2023 caua o 001 0 eng d |
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|a GW5XE
|b eng
|e rda
|e pn
|c GW5XE
|d EBLCP
|d YDX
|d OCLCF
|d OCLCQ
|d OCLCO
|d UKAHL
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| 020 |
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|a 9781484286678
|q (electronic bk.)
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|a 1484286677
|q (electronic bk.)
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|z 9781484286661
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7 |
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|a 10.1007/978-1-4842-8667-8
|2 doi
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| 035 |
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|a (OCoLC)1400015856
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| 050 |
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4 |
|a QA76.73.S67
|b B63 2023
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|a HCDD
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| 100 |
1 |
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|a Bobak, Angelo R.,
|e author.
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| 245 |
1 |
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|a SQL server analytical toolkit :
|b using windowing, analytical, ranking, and aggregate functions for data and statistical analysis /
|c Angelo Bobak.
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| 264 |
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|a [California] :
|b Apress,
|c [2023]
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| 300 |
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|a 1 online resource (xxiii, 1055 pages) :
|b illustrations.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 500 |
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|a Includes index.
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0 |
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|a Chapter 1: Partitions, Frames and the OVER() clause -- Chapter 2: Sales DW Use Case--Aggregate Functions -- Chapter 3: Sales Use Case - Analytical Functions -- Chapter 4: Sales Use Case - Ranking/Window Functions -- Chapter 5: Finance Use Case - Aggregate Functions -- Chapter 6: Finance Use Case - Ranking Functions -- Chapter 7: Finance Use Case - Analytical Functions -- Chapter 8: Plant Use Case - Aggregate Functions -- Chapter 9: Plant Use Case - Ranking Functions -- Chapter 10: Plant Use Case - Analytical Functions -- Chapter 11: Inventory Control Use Case - Aggregate Functions -- Chapter 12: Inventory Use Case - Ranking Functions -- Chapter 13: Inventory Use Case - Analytical Functions -- Chapter 14: Summary, Conclusions, and Next Steps -- Appendix 1: Function Syntax, Descriptions -- Appendix 2: Statistical Functions.
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|a Learn window function foundational concepts through a cookbook-style approach, beginning with an introduction to the OVER() clause, its various configurations in terms of how partitions and window frames are created, and how data is sorted in the partition so that the window function can operate on the partition data sets. You will build a toolkit based not only on the window functions but also on the performance tuning tools, use of Microsoft Excel to graph results, and future tools you can learn such as PowerBI, SSIS, and SSAS to enhance your data architecture skills. This book goes beyond just showing how each function works. It presents four unique use-case scenarios (sales, financial, engineering, and inventory control) related to statistical analysis, data analysis, and BI. Each section is covered in three chapters, one chapter for each of the window aggregate, ranking, and analytical function categories. Each chapter includes several TSQL code examples and is re-enforced with graphic output plus Microsoft Excel graphs created from the query output. SQL Server estimated query plans are generated and described so you can see how SQL Server processes the query. These together with IO, TIME, and PROFILE statistics are used to performance tune the query. You will know how to use indexes and when not to use indexes. You will learn how to use techniques such as creating report tables, memory enhanced tables, and creating clustered indexes to enhance performance. And you will wrap up your learning with suggested steps related to business intelligence and its relevance to other Microsoft Tools such as Power BI and Analysis Services. All code examples, including code to create and load each of the databases, are available online. What You Will Learn Use SQL Server window functions in the context of statistical and data analysis Re-purpose code so it can be modified for your unique applications Study use-case scenarios that span four critical industries Try tutorials on statistics, how to use SSMS, performance tuning, and basic TSQL queries in case you are new to TSQL or need a refresher Get started with statistical data analysis and data mining using TSQL queries to dive deep into data Follow prescriptive guidance on good coding standards to improve code legibility.
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|a Online resource; title from PDF title page (SpringerLink, viewed September 28, 2023).
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| 630 |
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|a SQL server.
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|a SQL server
|2 fast
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|a Database management.
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| 650 |
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7 |
|a Database management
|2 fast
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| 856 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-1-4842-8667-8
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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