Graph Database is simply an online database management system providing create, Read, Update, Delete (crud) operations that expose the graph data model and is a collection of nodes and edges, where each node represents an entity, and each edge represents a connection or relation between two nodes.
A unique identifier describes each node in a GDB, a set of incoming/outgoing edges and a set of properties expressed as key-value pair.
A database for storing, managing and querying highly connected and complex data.
Schema-less, based on graph theory
Only two types of data inside it i.e., nodes and relationships.
One of the NoSQL database management system, query by several languages, depends on database products.
Highly scalable, up to several billion nodes/properties/relationships.
Very fast when it comes to querying connected data.
Why Databases are important?
The importance of databases are mentioned below:
One of the reasons for choosing a graph database is that sheer performance increases when dealing with connected data versus RDBs and NoSQL stores. In RDBs, where join-intensive query performance degrades as the dataset grows more prominent, with a graph database performance tends to remain approximately steady even as the dataset grows. The reason for this better performance of Graph is that queries are restricted to a part of the Graph. As a result, the execution time for every query is proportional to the size of the Graph traversed rather than overall size.
Graphs are additive, we can add new relationships, new labels, new sub graphs and also new nodes to an existing structure without disturbing the current queries and application functionality.
Growing with graph database aligns perfectly with test-driven development practices, allowing graph database backend to develop in step with the rest of the application and any growing business requirements.
A lot of DB's have similar features and characteristics but what makes a graph database (GDB) unique is the mentioned two factor -
The underlying storage - Certain graph databases use native graph storage that Is optimized and developed for handling and storing Graph. Not all the database use native graph storage, some serialize the graph data into a relation Database or object-oriented database or some other general purpose data stores.
The processing engine - Some definition says that databases uses index- free adjacency means that connected nodes physically point to each other in Database. But let's take a broader view: any database that behaves like the GDB (i.e., exposes a graph model through (CRUD operations) from the user's perspective qualifies as a graph database. Native graph processing or index-free adjacency benefits traversal performance, but at the expense of making some queries that do not use traversals difficult.
Graph Database Use Cases
the below highlighted are the Use Cases of Graph Database
Fraud detection in Facebook and banks seems to work well. They Send notice to you when there is suspicious activity on your account.The fraud detection use case can be applied to cybersecurity intrusion detection as well. It has the benefit of sending analysis to look at only events that are significant and not Wasting resources on outlier as it uses intelligence tools, not hard-coded thresholds.
Identity and Access Management
This is a use case mentioned by Neo4j. It holds information about parties (like administrator, business units, end-users) and resources (like files, shares, network devices, agreements) for determining authorization that who can access and who can't access or manipulate resources By tracing from the individual through groups, roles, and product.
Network and IT operations
This is a default use case as network topology looks Like a graph, so makes sense to a Graph DB model.
Google is one who uses this model. In this, documents from different sources are collected up and then presented to the user in a way that is searchable.
The real world is more interconnected, and graph database Provides the best infrastructure to link diverse data. As it has easy expressions for the relationship between data, so it makes it easier for the programmer, user, and machine to understand the data and to find insights. Want to know more about databases we advice taking the subsequent steps -