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SQL Query Design Patterns and Best Practices

You're reading from  SQL Query Design Patterns and Best Practices

Product type Book
Published in Mar 2023
Publisher Packt
ISBN-13 9781837633289
Pages 270 pages
Edition 1st Edition
Languages
Authors (6):
Steve Hughes Steve Hughes
Profile icon Steve Hughes
Dennis Neer Dennis Neer
Profile icon Dennis Neer
Dr. Ram Babu Singh Dr. Ram Babu Singh
Profile icon Dr. Ram Babu Singh
Shabbir H. Mala Shabbir H. Mala
Profile icon Shabbir H. Mala
Leslie Andrews Leslie Andrews
Profile icon Leslie Andrews
Chi Zhang Chi Zhang
Profile icon Chi Zhang
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Toc

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 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

Adding queries in your Jupyter notebooks

Now that you’ve created your notebook, it is time to add code. Jupyter notebooks in Azure Data Studio support a variety of code languages, including SQL, Python, and R. It is beyond the scope of this book to delve into languages beyond SQL. However, you should be aware that notebooks support a variety of workloads throughout the Azure data ecosystem, particularly in Synapse Analytics.

There are a few steps to go through to get our SQL working as code in our notebooks.

Figure 13.8 – Notebook toolbar

Figure 13.8 – Notebook toolbar

Before we delve into adding code specifically, let’s do a quick review of the options available in the header of your notebook as shown in the previous screenshot:

  1. Cell provides the option to add a text or code cell to your notebook.
  2. The Run all option will run all the code in sequence in your notebook.
  3. Kernel specifies which language is the primary language of your notebook. In...
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