Data Management With Python, SQLite, and SQLAlchemy

Data Management With Python, SQLite, and SQLAlchemy

In this tutorial, you'll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL.

All programs process data in one form or another, and many need to be able to save and retrieve that data from one invocation to the next. Python, SQLite, and SQLAlchemy give your programs database functionality, allowing you to store data in a single file without the need for a database server.

You can achieve similar results using flat files in any number of formats, including CSV, JSON, XML, and even custom formats. Flat files are often human-readable text files—though they can also be binary data—with a structure that can be parsed by a computer program. Below, you’ll explore using SQL databases and flat files for data storage and manipulation and learn how to decide which approach is right for your program.

In this tutorial, you’ll learn how to use:

  • Flat files for data storage
  • SQL to improve access to persistent data
  • SQLite for data storage
  • SQLAlchemy to work with data as Python objects

You can get all of the code and data you’ll see in this tutorial by clicking on the link below:

Download the sample code: Click here to get the code you'll use to learn about data management with SQLite and SQLAlchemy in this tutorial.

Using Flat Files for Data Storage

flat file is a file containing data with no internal hierarchy and usually no references to external files. Flat files contain human-readable characters and are very useful for creating and reading data. Because they don’t have to use fixed field widths, flat files often use other structures to make it possible for a program to parse text.

For example, comma-separated value (CSV) files are lines of plain text in which the comma character separates the data elements. Each line of text represents a row of data, and each comma-separated value is a field within that row. The comma character delimiter indicates the boundary between data values.

Python excels at reading from and saving to files. Being able to read data files with Python allows you to restore an application to a useful state when you rerun it at a later time. Being able to save data in a file allows you to share information from the program between users and sites where the application runs.

Before a program can read a data file, it has to be able to understand the data. Usually, this means the data file needs to have some structure that the application can use to read and parse the text in the file.

Below is a CSV file named author_book_publisher.csv, used by the first example program in this tutorial:

first_name,last_name,title,publisher
Isaac,Asimov,Foundation,Random House
Pearl,Buck,The Good Earth,Random House
Pearl,Buck,The Good Earth,Simon & Schuster
Tom,Clancy,The Hunt For Red October,Berkley
Tom,Clancy,Patriot Games,Simon & Schuster
Stephen,King,It,Random House
Stephen,King,It,Penguin Random House
Stephen,King,Dead Zone,Random House
Stephen,King,The Shining,Penguin Random House
John,Le Carre,"Tinker, Tailor, Solider, Spy: A George Smiley Novel",Berkley
Alex,Michaelides,The Silent Patient,Simon & Schuster
Carol,Shaben,Into The Abyss,Simon & Schuster

The first line provides a comma-separated list of fields, which are the column names for the data that follows in the remaining lines. The rest of the lines contain the data, with each line representing a single record.

Note: Though the authors, books, and publishers are all real, the relationships between books and publishers are fictional and were created for the purposes of this tutorial.

Next, you’ll take a look at some of the advantages and disadvantages of using flat files like the above CSV to work with your data.

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