In this post, we will dive into MongoDB as a data store from a Python perspective. To that end, we'll write a simple script to showcase what we can achieve and any benefits we can reap from it.
Web applications, like many other software applications, are powered by data.
A commonly requested skill for software development positions is experience with NoSQL databases, including MongoDB. This tutorial will explore collecting data using an API, storing it in a MongoDB database, and doing some analysis of the data.
However, before jumping into the code let's take a moment to go
Spring Data is an umbrella project which contains many submodules, each specific to a particular database. In this article, we'll be covering Spring Data MongoDB by building an application that stores and retrieves data from MongoDB, a document based NO-SQL database.
If you'd like to read more about Spring
Edit: Updated code to Camo v0.12.1
First of all, Camo is a new class-based ES6 ODM for MongoDB and Node. With mainstream ES6 quickly approaching us, I thought we were long overdue for an ODM that took advantage of the new features, so I created Camo. What
Edit: Updated Camo code to v0.12.1
Camo is an ES6 ODM with class-based models. A few of its main features are: dead-simple schema declaration, intuitive schema inheritance, and support for multiple database backends.
A simple Camo model might look like this:
var Document = require('camo')
