In my previous post about Graph databases, we went through the introductions to NoSQL and Neo4j, tried to understand what a graph storage is, and how it makes our lives a little easier. We also had a sneak peek into the workings of the SQL through the example of the friends-of-friends scenario and looked how Cypher, the query language for Neo4J database, makes things simpler by easing the syntax, making your queries more readable and expressive at the same time.
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How do you feel about graphs? Graphs are all around us. The road network whose one arm probably passes from the front of your porch is an example. Your electric company’s grid network is as well. The neurons working in unison to keep the actions performed by our bodies coordinated in the form of the neural network are another example.
So, a very natural question comes to mind. What exactly are graphs, anyway? Why are they even needed? How do they make data visualization a piece of cake? These are the questions which will be touched on briefly in this article, along with the introduction to databases, their types, and a brief introduction to Neo4j.
You’ve probably heard of Jython before–a 13-year-old, mature, JVM-based, object-oriented scripting language, Jython is the ‘Java’nised version of Python, if you will. In the past, it went by the name JPython, a far cooler name, if you ask me!
Considering the time and effort that has been put into it in the recent years (the language development had a hiatus period during 2005- 2008/ 09); it has still not received the amount of attention from the programming community that it should have. This is one language that is laden with features and is the best of both worlds: Java and Python.