Book Image

SQL Query Design Patterns and Best Practices

By : Steve Hughes, Dennis Neer, Dr. Ram Babu Singh, Shabbir H. Mala, Leslie Andrews, Chi Zhang
5 (1)
Book Image

SQL Query Design Patterns and Best Practices

5 (1)
By: Steve Hughes, Dennis Neer, Dr. Ram Babu Singh, Shabbir H. Mala, Leslie Andrews, Chi Zhang

Overview of this book

SQL has been the de facto standard when interacting with databases for decades and shows no signs of going away. Through the years, report developers or data wranglers have had to learn SQL on the fly to meet the business needs, so if you are someone who needs to write queries, SQL Query Design and Pattern Best Practices is for you. This book will guide you through making efficient SQL queries by reducing set sizes for effective results. You’ll learn how to format your results to make them easier to consume at their destination. From there, the book will take you through solving complex business problems using more advanced techniques, such as common table expressions and window functions, and advance to uncovering issues resulting from security in the underlying dataset. Armed with this knowledge, you’ll have a foundation for building queries and be ready to shift focus to using tools, such as query plans and indexes, to optimize those queries. The book will go over the modern data estate, which includes data lakes and JSON data, and wrap up with a brief on how to use Jupyter notebooks in your SQL journey. By the end of this SQL book, you’ll be able to make efficient SQL queries that will improve your report writing and the overall SQL experience.
Table of Contents (21 chapters)
1
Part 1: Refining Your Queries to Get the Results You Need
6
Part 2: Solving Complex Business and Data Problems in Your Queries
11
Part 3: Optimizing Your Queries to Improve Performance
14
Part 4: Working with Your Data on the Modern Data Platform

Summary

In this chapter, we learned what the OPENROWSET(BULK...) function is and how to use this function to pull data from the Storage service directly into the Synapse Analytics tabular structure without having to load it into a table. After the OPENROWSET bulk function, we looked at external tables and detailed three steps to create an external table on a serverless SQL pool with a similar functionality of pulling content from remote files in storage.

Both the OPENROWSET bulk function and external tables are very powerful tools to connect directly to Azure Storage services and don’t need to be converted into tables or build pipelines to make data available for further engineering/analytics work.

In our next chapter, we will move our work to Jupyter Notebook, and look at how to create notebooks, write queries, create documentation from inside the notebook, and share them with team members.