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)
Part 1: Refining Your Queries to Get the Results You Need
Part 2: Solving Complex Business and Data Problems in Your Queries
Part 3: Optimizing Your Queries to Improve Performance
Part 4: Working with Your Data on the Modern Data Platform

Improving performance when aggregating data

Developing SQL queries to aggregate data is a relatively simple process if you understand the granularity that you want to achieve. But there are times that you will need to rework your SQL to enable it to perform more efficiently; this mostly happens when there are many columns that are part of many aggregations. For example, if the result set contains aggregations that are part of another aggregation, you would want to develop the SQL query containing a subquery that creates the initial aggregations and then performs the final aggregation. An alternative would be to create multiple queries to aggregate the data appropriately for each aggregation and then use a MERGE function to create a single dataset to be able to perform your analysis. Here is a sample SQL query that uses subqueries to create an aggregation from two different subjects:

SELECT YEAR([Invoice Date Key]) as [Invoice Year]