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

Reducing Rows and Columns in Your Result Sets

Today the sources of data that a data analyst has access to have grown to the point that the amount of data that is available to you is unlimited. The challenge that a data analyst faces today is to determine how to generate a result set that is manageable and has the information that ensures that it will meet the needs of the analyst for their reports and analysis. If there is too much data, the result set will become unmanageable and unusable due to information overload; too little data and the data will have gaps, and the end user will lose trust in the data.

In this chapter, we will review how you determine how much data and what data you should keep in your result set and how to filter that data efficiently. We will also review how to determine which columns you should keep and how you can efficiently select the correct columns. The chapter will then wrap up with a short discussion on how these activities will impact future data aggregations.

By the end of this chapter, you will understand how to identify the data and columns that you need and the most efficient method for getting the data into a usable result set that can easily be recreated.

In this chapter, we will cover the following main topics:

  • Identifying data to be removed from the dataset
  • Understanding the value of creating views versus removing data
  • Exploring the impact of row and column reductions on aggregations