Foundations for Analytics with Python

Chapter 4. Databases

Like spreadsheets, databases are ubiquitous in business. Companies use databases to store data on customers, inventory, and employees. Databases are vital to tracking operations, sales, financials, and more. What sets a database apart from a simple spreadsheet or a workbook of spreadsheets is that a database’s tables are linked such that a row in one spreadsheet can be linked to a row or column in another. To give a standard example, customer data—name, address, and so on—may be linked (using a customer ID number) to a row in an “orders” spreadsheet that contains items ordered. Those items are in turn linked up to data in your “suppliers” spreadsheet, enabling you to track and fulfill orders—and also to perform deeper analytics. While CSV and Excel files are common, important data sources that you can process automatically and at scale with Python, and building skills to handle these files has been important both from a learning perspective (to learn common programming operations) and from a practical perspective (a great deal of business data is stored in these types of files), databases truly leverage the power of computers to execute tasks hundreds, thousands, or even millions of times.

Relational Databases

This chapter will deal with relational databases and relational database management systems (RDBMSs), where tables of information are connected by defined relationships among different tables, using keys like “order ID number” to connect a customer ...