unlearning-sql

Components and examples from the Unlearning SQL book.

Installation

We recommend installing uv to manage the virtual environment for this project. See https://docs.astral.sh/uv/. Follow the Installing uv instructions, if you don't already have uv.

1. Clone or download this Git repository, https://github.com/slott56/unlearning-sql.

   git clone https://github.com/slott56/unlearning-sql.git

2. Change to the downloaded directory.

   cd unlearning-sql

3. Install required packages to run the demo applications.

   uv sync

4. Optionally, install the packages to do development work, like unit testing or running the acceptance test suite.

   uv sync --dev

Activate the Virtual Environment

It's essential to activate the virtual environment for this (or any) Python project before doing any real work.

source .venv/bin/activate

For Windows users, the command is slightly different. See the https://docs.python.org/3/library/venv.html documentation for the PowerShell command.

General Operation

In order to play with the examples, it's helpful to have a file named activate_source.csv. This file contains the raw data to be bulk-loaded into the database.

It's also helpful, of course, to have a database. In this case, it will be unlearning_sql.db.

To build the file, and the database, execute the following two steps:

python src/fake_data.py
python src/sql_db_preparation.py

The fake_data.py program builds the activate_source.data file. The sql_db_preparation.py program loads the database with some (but not all) of the fake data. This omission of data permits some of the data validation rules to spot bad data in the input.

Makefile

The Makefile has targets to help create files and databases.

make sql_load

The sql_load target will build the fake data and populate the database if needed. It will run the SQL-based loader application.

make python_load

The python_load target will build the fake data and populate the database if needed. It will run the Python-based loader application.

Diagrams

The diagrams were built with PlantUML. See https://plantuml.com for more information. This is not required to run the demonstration applications. It's handy for creating entity-relationship diagrams.