You're reading from SQL Query Design Patterns and Best Practices A practical guide to writing readable and maintainable SQL queries using its design patterns

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781837633289

Length 270 pages

Edition 1st Edition

Languages

SQL

Tools

Jupyter

Concepts

Database Programming

Authors (6):

Chi Zhang

Steven Hughes

Shabbir Mala

Dennis Neer

Leslie Andrews

Ram Babu Singh

+2 more

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Table of Contents (21) Chapters

Preface

1. Part 1: Refining Your Queries to Get the Results You Need

2. Chapter 1: Reducing Rows and Columns in Your Result Sets FREE CHAPTER

3. Chapter 2: Efficiently Aggregating Data

4. Chapter 3: Formatting Your Results for Easier Consumption

5. Chapter 4: Manipulating Data Results Using Conditional SQL

6. Part 2: Solving Complex Business and Data Problems in Your Queries

7. Chapter 5: Using Common Table Expressions

8. Chapter 6: Analyze Your Data Using Window Functions

9. Chapter 7: Reshaping Data with Advanced Techniques

10. Chapter 8: Impact of SQL Server Security on Query Results

11. Part 3: Optimizing Your Queries to Improve Performance

12. Chapter 9: Understanding Query Plans

13. Chapter 10: Understanding the Impact of Indexes on Query Design

14. Part 4: Working with Your Data on the Modern Data Platform

15. Chapter 11: Handling JSON Data in SQL Server

16. Chapter 12: Integrating File Data and Data Lake Content with SQL

17. Chapter 13: Organizing and Sharing Your Queries with Jupyter Notebooks

18. Index

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19. Other Books You May Enjoy

Appendix: Preparing Your Environment

Prerequisites for running the exercises

Efficiently Aggregating Data

In the previous chapter, you learned how to reduce the number of rows and columns contained in your dataset so that you have the data required to do the analysis that you are interested in. The next step that you will want to do is to aggregate your data so that you have summarized information making it easier to show the information in an easy-to-consume dataset.

In this chapter, you will learn the what, when, and how of data aggregation in SQL so that the dataset can be used by most end users to do successful analytics. We will start with what data should be aggregated, followed by a discussion on when the data should be aggregated and the different methods of aggregating the data. The chapter will wrap up with a short discussion on how to develop aggregations that are efficient and have minimal impact on your SQL server resources.

In this chapter, we will cover the following main topics:

Identifying data to be aggregated
Determining...

The rest of the chapter is locked

Authors (7)

Steven Hughes

Steve Hughes is a senior director of data and analytics at 3Cloud. In this role, he focuses on growing the teams' skills and capabilities to deliver data projects with Azure. In his 20 years of experience in technology, much of the time was spent on creating business intelligence (BI) solutions and helping customers implement BI and cloud solutions. He is passionate about helping customers understand that data is valuable and profitable. Steve has recently been diagnosed with ALS but continues to work and share with others what he has learned. Steve is also the founder of Data on Wheels where he blogs with one of his daughters on topics such as data, analytics, and work enablement

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Steve Hughes

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Zhang

Chi Zhang is a lead data architect at 3Cloud. After completing her master's degree at Carnegie Mellon University, she worked in data consulting for the past 5 years. She has helped clients from various industries to deliver customized data solutions within Azure. Focusing on ETL, data warehousing, data modeling, and BI reporting, Chi solves data problems for clients and builds data solutions that provide business users with better operational insights and a smoother experience. Recently, she has continued to grow her footprint in the Azure community: earning certifications in Azure data engineering and data science, giving her first public tech talk, co-authoring a technical book, and becoming an active contributor to the SQL community

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Andrews

Andrews is a lead data architect of data and analytics at 3Cloud. Working in the IT sector for over 20 years in local government, electrical supply, law enforcement, and healthcare, she has broad experience to draw from to assist clients in making data more accessible for end users. Leslie's expertise includes SQL development, ETL, data warehousing, data modeling, and analytics. Leslie currently holds several Microsoft certifications related to Azure technologies for data engineering. She approaches each day as an opportunity to use data and technology to make it easier for others to do their daily work. Leslie has been doing public speaking since 2015, was an IDERA ACE in 2019, and is a supporter of and contributor to the SQL community

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Shabbir Mala

Shabbir H. Mala is a director of data and analytics at 3Cloud. He has over 23 years of experience in thought leadership and consulting, developing complex data solutions, business systems, and processes using Azure Data Services and Power BI. He currently manages a team of over 40 principals and senior architects focusing on business growth, successful project delivery, and client experience. He has done talks at Power BI conferences as well as user groups. He has been awarded Microsoft FastTrack Solution Architect in 2021 and 2022. He is currently living in Chicago, married, and has three beautiful kids

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Ram Babu Singh

Dr. Ram Babu Singh is a Microsoft Certified Professional with a Ph.D. in computer science. He is a lead architect of data and analytics at 3Cloud, using complex data analytics and data science solutions. In his 20 years of career, over a decade was spent in leadership positions providing data science and data platform solutions to the world's top IT consulting companies. He has been published in international journals and has a patent in his name

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Neer

Dennis Neer is a senior architect of data and analytics at 3Cloud. In this role, he focuses on working with clients to design Azure solutions for their data and analytic needs to improve their business decision-making process. This includes using tools such as SQL Server databases, Synapse, data lakes, and Power BI. In his 30 years of experience in technology, much of the time was spent on designing and building database and visualization solutions. He is passionate about helping businesses to understand data and use it to their advantage during their decision-making process

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You're reading from SQL Query Design Patterns and Best Practices A practical guide to writing readable and maintainable SQL queries using its design patterns

Table of Contents (21) Chapters

Efficiently Aggregating Data

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Authors (7)

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