Book Image

The Applied SQL Data Analytics Workshop - Second Edition

By : Matt Goldwasser, Upom Malik, Benjamin Johnston
3.5 (2)
Book Image

The Applied SQL Data Analytics Workshop - Second Edition

3.5 (2)
By: 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. Hidden in this data are key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. Are you ready to enter the exciting world of data analytics and unlock these useful insights? Written by a team of expert data scientists who have used their data analytics skills to transform businesses of all shapes and sizes, The Applied SQL Data Analytics Workshop is a great way to get started with data analysis, showing you how to effectively sieve and process information from raw data, even without any prior experience. The book begins by showing you how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you'll learn how to write SQL queries to aggregate, calculate and combine SQL data from sources outside of your current dataset. You'll also discover how to work with different data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you'll finally 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 The Applied SQL Data Analytics Workshop, you'll have the skills you need to start identifying patterns and unlocking insights in your own data. You will be capable of looking and assessing data with the critical eye of a skilled data analyst.
Table of Contents (9 chapters)
7. The Scientific Method and Applied Problem Solving

The COPY Command

At this point, you are probably familiar with the SELECT statement (covered in Chapter 1, Introduction to SQL for Analytics), which allows us to retrieve data from our database. While this command is useful for small datasets that can be scanned quickly, we will often want to save a large dataset to a file. By saving these datasets to files, we can further process or analyze the data locally using Excel, Python, or R. In order to retrieve these large datasets, we can use the Postgres COPY command, which efficiently transfers data from a database to a file, or from a file to a database.

The COPY statement retrieves data from your database and dumps it into the file format that you choose. For example, consider the following statement:


The following is the output of the code:

Figure 4.1: Using COPY to print the results to STDOUT in a CSV file format

This statement returns...