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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

Creating Tables with Solid Structures

Most of the operations in a relational database are organized around tables and the data inside them. The operations in the lifecycle of data generally can be categorized into four groups—create, read, update, and delete, or CRUD. To utilize any data, you must create the definition of the table first, then populate the table with data records. Once a dataset is created, you can read all aspects of information from it. If there is any need to change the data, you need to update the affected records. After the update operation, you may need to read the updated data again. And finally, when you do not need the data anymore, you will want to delete the records to save storage costs and increase performance. If you do not need this dataset, you can even delete the whole dataset by removing its definition from the database. These CRUD operations are the most common data management activities, and are demonstrated in the following figure...

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SQL for Data Analytics
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