Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
9
9. Using SQL to Uncover the Truth – a Case Study

Summary

In this chapter, we have covered a wide variety of topics all designed to help us understand and improve the performance of our SQL queries. The chapter began with a thorough discussion of the query planner, including the EXPLAIN and ANALYZE statements, as well as various indexing methods. We discussed a number of different compromises and considerations that can be made to reduce the time taken to execute queries. We considered a number of scenarios where indexing methods would be of benefit and others where the query planner may disregard the index, thus reducing the efficiency of the query. We then moved on to the use of joins to efficiently combine information from a number of different tables and ended with an in-depth look at functions and automatic function calls through the use of triggers.

In the next chapter, we will combine all of the topics we have covered thus far in a final case study, applying our SQL knowledge and the scientific method in general, as we solve...