東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

SQL server analytical toolkit = usin...

Bobak, Angelo R.

FindBook

Google Book

Amazon

博客來

SQL server analytical toolkit = using windowing, analytical, ranking, and aggregate functions for data and statistical analysis /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	SQL server analytical toolkit/ by Angelo Bobak.
其他題名:	using windowing, analytical, ranking, and aggregate functions for data and statistical analysis /
作者:	Bobak, Angelo R.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	xxiii, 1055 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Database management. -
電子資源:	https://doi.org/10.1007/978-1-4842-8667-8
ISBN:	9781484286678

SQL server analytical toolkit = using windowing, analytical, ranking, and aggregate functions for data and statistical analysis /
Bobak, Angelo R.

SQL server analytical toolkitusing windowing, analytical, ranking, and aggregate functions for data and statistical analysis /[electronic resource] :by Angelo Bobak. - Berkeley, CA :Apress :2023. - xxiii, 1055 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484286678

Standard No.: 10.1007/978-1-4842-8667-8doiSubjects--Uniform Titles:

SQL server.
Subjects--Topical Terms:

527442
Database management.

LC Class. No.: QA76.73.S67 / B63 2023

Dewey Class. No.: 005.7585

SQL server analytical toolkit = using windowing, analytical, ranking, and aggregate functions for data and statistical analysis /
LDR:04217nmm a2200325 a 4500 001 2334964
003 DE-He213
005 20230923122514.0
006 m d
007 cr nn 008maaau
008 240402s2023 cau s 0 eng d
020 $a 9781484286678 $q (electronic bk.)
020 $a 9781484286661 $q (paper)
024 7 $a 10.1007/978-1-4842-8667-8 $2 doi
035 $a 978-1-4842-8667-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.S67 $b B63 2023
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 005.7585 $2 23
090 $a QA76.73.S67 $b B663 2023
100 1 $a Bobak, Angelo R. $3 3666967
245 1 0 $a SQL server analytical toolkit $h [electronic resource] : $b using windowing, analytical, ranking, and aggregate functions for data and statistical analysis / $c by Angelo Bobak.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxiii, 1055 p. : $b ill., digital ; $c 24 cm.
505 0 $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.
520 $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.
630 0 0 $a SQL server. $3 542688
650 0 $a Database management. $3 527442
650 1 4 $a Microsoft. $3 3593799
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Statistics and Computing. $3 3594429
650 2 4 $a Data Engineering. $3 3409361
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8667-8
950 $a Professional and Applied Computing (SpringerNature-12059)