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  • Book Overview & Buying SQL for Data Analytics
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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
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SQL for Data Analytics

SQL for Data Analytics

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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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

Exchanging Data Using COPY

In the previous chapter, you learned about the SQL CREATE, INSERT, and DROP operations in the CRUD process. You also learned how to populate the created tables with manually written values or values from other tables. These operations are good enough to work on small datasets inside a database. However, in real-world scenarios, the bulk of the data comes from systems outside of the database. For example, different systems within the same company need to exchange data, and business analysts need to utilize data from external vendors for analytics. This data is usually sent in the form of files. In addition, once you finish your analytical tasks, you also need to share your results with internal teams or external customers. This data sharing is often done using files, too. While it is possible to use SQL statements to read from or write to these files, they are generally slow and are limited in file processing power.

Typically, data transfer is done with...

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SQL for Data Analytics
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