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SQL for Data Analytics

You're reading from  SQL for Data Analytics

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Pages 386 pages
Edition 1st Edition
Languages
Authors (3):
Upom Malik Upom Malik
Profile icon Upom Malik
Matt Goldwasser Matt Goldwasser
Profile icon Matt Goldwasser
Benjamin Johnston Benjamin Johnston
Profile icon Benjamin Johnston
View More author details

Table of Contents (11) Chapters

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics 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

Using JSON Data Types in Postgres

While arrays can be useful for storing a list of values in a single field, sometimes our data structures can be complex. For example, we might want to store multiple values of different types in a single field, and we might want data to be keyed with labels rather than stored sequentially. These are common issues with log-level data, as well as alternative data.

JavaScript Object Notation (JSON) is an open standard text format for storing data of varying complexity. It can be used to represent just about anything. Similar to how a database table has column names, JSON data has keys. We can represent a record from our customers database easily using JSON, by storing column names as keys, and row values as values. The row_to_json function transforms rows to JSON:

SELECT row_to_json(c) FROM customers c limit 1;

Here is the output of the preceding query:

{"customer_id":1,"title":null,"first_name":"Arlena"...
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