NoSQL brought flexibility to the tabular world of databases. MongoDB in particular became an excellent option to store unstructured JSON documents. Data starts as JSON in the UI and undergoes very few transformations to be stored, so we get benefits from increased performance and decreased processing time.
But NoSQL
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')
Understand your data better with visualizations! With over 330+ pages, you'll learn the ins and outs of visualizing data in Python with popular libraries like Matplotlib, Seaborn, Bokeh, and more.
Includes a free 30 page Seaborn guide!
