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SQL for Data Analytics

SQL for Data Analytics - Fourth Edition

By : Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik
4.5 (2)
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SQL for Data Analytics

SQL for Data Analytics

4.5 (2)
By: Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik

Overview of this book

SQL remains one of the most essential tools for modern data analysis and mastering it can set you apart in a competitive data landscape. This book helps you go beyond basic query writing to develop a deep, practical understanding of how SQL powers real-world decision-making. SQL for Data Analytics, Fourth Edition, is for anyone who wants to go beyond basic SQL syntax and confidently analyze real-world data. Whether you're trying to make sense of production data for the first time or upgrading your analytics toolkit, this book gives you the skills to turn data into actionable outcomes. You'll start by creating and managing structured databases before advancing to data retrieval, transformation, and summarization. From there, you’ll take on more complex tasks such as window functions, statistical operations, and analyzing geospatial, time-series, and text data. With hands-on exercises, case studies, and detailed guidance throughout, this book prepares you to apply SQL in everyday business contexts, whether you're cleaning data, building dashboards, or presenting findings to stakeholders. By the end, you'll have a powerful SQL toolkit that translates directly to the work analysts do every day. *Email sign-up and proof of purchase required
Table of Contents (21 chapters)
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Lock Free Chapter
1
Part 1: Data Management Systems
6
Part 2: Data Presentation and Manipulation
12
Part 3: Advanced Topics on Analytics
19
Other Books You May Enjoy
20
Index

Summary

This chapter covered the important topic of optimizing SQL queries for performance. It explained the various methods for scanning databases, discussed the PostgreSQL server query planner, and introduced the EXPLAIN command and the EXPLAIN ANALYZE command, which were used to display the plan for a query. With these topics, you should now have a better understanding of the data retrieval mechanisms for PostgreSQL, which will help you write optimized queries that have better performance.

In the next two chapters, you will dive into the processing of specific data types. In Chapter 11, you will start with JSON and Array. Then, in Chapter 12, you will move on to Date, Text, and Geographical data.

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SQL for Data Analytics
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