Data

On an early post, I described a non-exhaustive taxonomy of data store types, as well as the types of problem domains each one was best suited for. On this post, I will address some approaches to modelling data in graph data stores, particularly with Neo4j. Graph data stores have been increasingly adopted over the past couple of years in several business domains, ranging from logistics to bio-informatics. Their power lies in their ability to model complex networks and tree structures, with data points ranging from hundreds to millions of nodes and edges. ...

This post will list a non-exhaustive taxonomy of data store types, and outline how they can be used to model different problem domains. Data Modelling In database design, modelling can be defined as the process of mapping the entities and events from a particular domain, into a representational format that can be stored into a database. The goal is to be able to answer relevant questions with data once it’s stored. Suitably, when model our domain, we must think of data in the way that it will be processed, as opposed to presented; and depending on what our domain is, some data store types may be more practical in helping us answer certain types of questions better than other. Understanding the strengths and weaknesses of each data store type can greatly ease the decision of which would be more suitable for a particular task we have in hand. ...