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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
Author Profile Icon Ram Babu Singh
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

Creating Jupyter notebooks in Azure Data Studio

Most data professionals who specialize in SQL are not familiar with notebooks. Jupyter notebooks have typically been in the domain of data science and data lakes. When working with data lakes, it is common to use code such as Python and deploy it using notebooks with tools such as Databricks. If you have not used Databricks or other notebook-supported tools, this will be your first exposure to one. If you’re familiar with notebooks already, keep reading; you may find they are implemented in Azure Data Studio a bit differently from the tools that you are familiar with.

Understanding notebooks in Azure Data Studio

Azure Data Studio natively supports both Jupyter books and Jupyter notebooks. When starting to use notebooks inside of Azure Data Studio, the instinctive way to interact with them is by using the NOTEBOOKS option in the side menu, as shown here:

Figure 13.1 – The NOTEBOOKS option on the Azure Data Studio sidebar

Figure 13.1 – The NOTEBOOKS option...

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