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

Database Scanning Methods

You have learned that all database operations are carried out by database management systems (DBMSs) such as PostgreSQL. Typically, the DBMS will run these operations in a server's memory, which stores the data to be processed. The problem with this approach is that memory storage is not large enough for modern databases, which are frequently in a scale of gigabytes, if not terabytes. Data in the majority of modern databases is saved on hard disks and uploaded into memory when it is used in a database operation. Yet again, a DBMS can only upload a small part of the database into memory. Whenever it figures that it needs a certain dataset, it must go to the hard disk to retrieve the unit of storage (which is called a hard disk block) that has the required data in it. The process that the PostgreSQL server uses to search through a database is known as scanning.

SQL-compliant databases, such as PostgreSQL, provide several different methods for scanning...