Book Image

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston
Book Image

SQL for Data Analytics - Third Edition

By: Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth: A Case Study

The COPY Command

At this point, you are probably familiar with the SELECT statement (covered in Chapter 2, The Basics of SQL for Analytics), which allows you to retrieve data from a database. While this command is useful for small datasets that can be scanned quickly, you will often want to save a large dataset to a file. By saving these datasets to files, you can further process or analyze the data locally using Excel or Python. To retrieve these large datasets, you can use the PostgreSQL COPY command, which efficiently transfers data from a database to a file, or from a file to a database. This COPY command must be executed when connected to the PostgreSQL database using a SQL client, such as the PostgreSQL psql command. In the next section, you will learn how to use the psql command, then you will learn how to copy data with it.

Running the psql Command

You have been using the pgAdmin frontend client to access your PostgreSQL database, and you have briefly used the psql tool...