Graph Based Data Model in NoSQL

Graph Based Data Model in NoSQL is a type of Data Model which tries to focus on building the relationship between data elements. As the name suggests Graph-Based Data Model, each element here is stored as a node, and the association between these elements is often known as Links. Association is stored directly as these are the first-class elements of the data model. These data models give us a conceptual view of the data.

These are the data models which are based on topographical network structure. Obviously, in graph theory, we have terms like Nodes, edges, and properties, let’s see what it means here in the Graph-Based data model.

• Nodes: These are the instances of data that represent objects which is to be tracked.
• Edges: As we already know edges represent relationships between nodes.
• Properties: It represents information associated with nodes.

The below image represents Nodes with properties from relationships represented by edges.

Working of Graph Data Model : 

In these data models, the nodes which are connected together are connected physically and the physical connection among them is also taken as a piece of data. Connecting data in this way becomes easy to query a relationship. This data model reads the relationship from storage directly instead of calculating and querying the connection steps. Like many different NoSQL databases these data models don’t have any schema as it is important because schema makes the model well and good and easy to edit.

Examples of Graph Data Models :

• JanusGraph: These are very helpful in big data analytics. It is a scalable graph database system open source too. JanusGraph has different features like:
  • Storage: Many options are available for storing graph data like Cassandra. 
  • Support for transactions: There are many supports available like ACID (Atomicity, Consistency, Isolation, and Durability) which can hold thousands of concurrent users.
  • Searching options: Complex searching options are available and optional support too.
• Neo4j: It stands for Network Exploration and Optimization 4 Java. As the name suggests this graph database is written in Java with native graph storage and processing. Neo4j has different features like:
  • Scalable: Scalable through data partitioning into pieces known as shards.
  • Higher Availability: Availability is very much high due to continuous backups and rolling upgrades.
  • Query Language: Uses programmer-friendly query language Cypher graph query language.DGraph main features are:
• DGraph: It is an open-source distributed graph database system designed with scalability.
  • Query Language: It uses GraphQL, which is solely made for APIs.
  • open-source system: support for many open standards.

Advantages of Graph Data Model :

• Structure: The structures are very agile and workable too.
• Explicit Representation: The portrayal of relationships between entities is explicit.
• Real-time O/P Results: Query gives us real-time output results.

Disadvantages of Graph Data Model :

• No standard query language: Since the language depends on the platform that is used so there is no certain standard query language.
• Unprofessional Graphs: Graphs are very unprofessional for transactional-based systems.
• Small User Base: The user base is small which makes it very difficult to get support when running into a system.

Applications of Graph Data Model:

• Graph data models are very much used in fraud detection which itself is very much useful and important.
• It is used in Digital asset management which provides a scalable database model to keep track of digital assets.
• It is used in Network management which alerts a network administrator about problems in a network.
• It is used in Context-aware services by giving traffic updates and many more.
• It is used in Real-Time Recommendation Engines which provide a better user experience.