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SQL Query Design Patterns and Best Practices

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

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781837633289
Length 270 pages
Edition 1st Edition
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Authors (6):
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Chi Zhang Chi Zhang
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Chi Zhang
Steven Hughes Steven Hughes
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Steven Hughes
Shabbir Mala Shabbir Mala
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Shabbir Mala
Dennis Neer Dennis Neer
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Dennis Neer
Leslie Andrews Leslie Andrews
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Leslie Andrews
Ram Babu Singh Ram Babu Singh
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Ram Babu Singh
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Table of Contents (21) Chapters Close

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 19. Other Books You May Enjoy Appendix: Preparing Your Environment

Identifying data to be aggregated

To identify the data that you are going to aggregate in the initial dataset that you have created, it is important to understand the story that you are trying to tell about the data. Some items will need to be counted, some will need to be summarized, and some will need to be aggregated by summarizing and counting. For example, if you are interested in doing an analysis of the data showing how many times an item has been ordered, then this would be an example of when you would aggregate the data by performing counts. If you are looking at data to see how many sales have occurred or the profits that have been made, this would be an example of summarizing the data. In addition, you may be interested in the average profit for a time period or the earliest and latest that an order has been delivered.

With these aggregations in place, the size of the dataset will be reduced, and you will be able to perform several other calculations based on these aggregations...

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