Coffee Shop Community Library (CSCL) Database

Getting Started

This repository contains starter data for the CSCL Mini Project for the following database engines:

• MongoDB (NoSQL)
• MySQL
• PostgreSQL

Mongo

Install Mongo Server

By default Mongo will not have access control enabled. You will create users and grant permissions. Please refer to the Mongo documentation.

Importing the Database

You can find the CSCL Mongo database data in the /mongo directory. The easiest way to import this data is to use Mongo Compass.

1. Create a new database named cscl with a collection named books.
2. Click your way to the books collection under the new database.
3. Select Add Data then import file
4. For the 'File Path' select the /mongo/cscl_db.json file from this repository. For 'File Type' select JSON. Leave the options as-is.

####For Data Science Fellows: There is a Mongo database that contains 2020 checkout historical data to use for your data science needs. Fellow the instructions below to add it into Mongo compass:

1. In the database named cscl, create a new collection called 2020_historical_data
2. Click your way to the 2020_historical_data collection under the database.
3. Select Add Data then import file
4. For the 'File Path' select the /mongo/cscl_2020_top_hundred_books.json file from this repository. For 'File Type' select JSON. Leave the options as-is.

MariaDB (MySql)

Install MariaDB

Importing the Database

You can find the CSCL SQL dump in /mysql/cscl_db.sql. There are many ways to import a MySQL database:

Using DBeaver
Using the Command Line

####For Data Science Fellows: Once you import the data using the methods above, you will find a table named 2020_CHECKOUT_DATA_TOP_HUNDRED_PER_WEEK. This is where your historical data is found.

PostgreSQL

Install PostgreSQL on Mac
Install PostgreSQL on Linux
Install PostgreSQL on Windows

Importing the Database

You can find the CSCL SQL dump in /postgres/cscl_db.sql. There are many ways to import a PostgreSQL database:

Using DBeaver
Using the Command Line

####For Data Science Fellows: Once you import the data using the methods above, you will find a table named 2020_CHECKOUT_DATA_TOP_HUNDRED_PER_WEEK. This is where your historical data is found.

## Tools

Mongo

Mongo Compass (Mongo GUI Application)

MariaDB / PostgreSQL

DBeaver (SQL GUI Application)

MariaDB

MySQL WorkBench