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

Determining when data should be aggregated

Now that we know what data we want to aggregate, when should this aggregation be performed? The aggregation should occur once you have identified the level of granularity you require. So, what does the level of granularity mean? It refers to the level of detail that an aggregation is completed to; for example, you want to know your profits at a daily level or monthly level. Other examples include aggregating to the day, month, year, store location, product, and so on.

Going back to Figure 2.1, we have the aggregations based on the invoice date, but we really want to know the totals based on the year, so you would then want to perform the aggregation based on the created dataset. Refer to the following query for how to do this:

SELECT YEAR([Invoice Date Key]) as Year
      ,SUM([Quantity]) as "# of items sold"
      ,SUM([Profit]) as profit