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SQL for Data Analytics

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser , Upom Malik , Benjamin Johnston
4.8 (53)
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SQL for Data Analytics

SQL for Data Analytics

4.8 (53)
By: Jun Shan, Matt Goldwasser , Upom Malik , Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.
Table of Contents (11 chapters)
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9
9. Using SQL to Uncover the Truth: A Case Study

Summary

In this chapter, you have covered a wide variety of topics designed to help you understand and improve the performance of your 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. You discussed different compromises and considerations that can be made to reduce the time needed to execute queries. You considered several 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. You then moved on to learn how to kill long-running queries. You also covered an in-depth look at functions and automatic function calls using triggers and learned about the \df and \sf commands.

In the next chapter, you will combine all the topics you have covered thus far in a final case study, applying your SQL knowledge and the scientific method in general, as you solve a real-world problem...

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SQL for Data Analytics
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