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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Upom Malik Upom Malik
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Upom Malik
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Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth – a Case Study Appendix

Assembling Data

Connecting Tables Using JOIN

In Chapter 2, The Basics of SQL for Analytics, we discussed how we can query data from a table. However, the majority of the time, the data you are interested in is spread across multiple tables. Fortunately, SQL has methods for bringing related tables together using the JOIN keyword.

To illustrate, let's look at two tables in our database – dealerships and salespeople. In the salespeople table, we observe that we have a column called dealership_id. This dealership_id column is a direct reference to the dealership_id column in the dealerships table. When table A has a column that references the primary key of table B, the column is said to be a foreign key to table A. In this case, the dealership_id column in salespeople is a foreign key to the dealerships table.

Note

Foreign keys can also be added as a column constraint to a table in order to improve the integrity of the data by making sure that the foreign key never...

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