In the previous articles we have discussed about three common databases used in data sciences – Column-oriented Database, Key-Value Database and Document-Oriented Database. A graph database portrays the data like they are viewed conceptually. This graph is of mathematics. A graph in mathematics is not a bar or line chart. It is like a mind map. The graph paradigm is a reimagination of what is possible with the idea of connections. A graph has two elements – a node and a relationship. Based on the concept of a mathematical graph, a graph database contains a collection of nodes and edges. Each node represents an entity and each relationship represents how two nodes are associated. Properties are germane information to nodes. Every node in a graph database is defined by a unique identifier, a set of outgoing edges and/or incoming edges and a set of properties expressed as key/value pairs. Graph databases use vertex and edge to describe data. This is how the graph database is illustrated :
So, we can say that a graph database is a system with Create, Read, Update and Delete (CRUD) operations working on a graph data model. They are usually built for use with transactional (OLTP) systems. So, they are optimized for transactional performance, has transactional integrity and operational availability. Unlike other databases, relationships are the priority in graph databases. Some of the graph databases use native graph storage which is designed to store and manage graphs, while the rest use relational or object-oriented databases. Native graph processing is an efficient means of processing graph data. Other CRUD operations which are not optimized for graphs, often results in reduced performance.
Graph Database can quickly find all the relationship paths between two things, customer-centric data views, recommender systems, fraud detection, supply chain problems etc. The graph makes solving analytical tasks much cheaper. Developing with graph technology aligns with agile development practices.
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