Graph databases are becoming more popular as businesses strive to find new and innovative ways to store and analyze data. As per the reports, the graph database market size was valued at $651 million in 2018.
But, what is a graph database, and what makes it unique?
This post will discuss the types of graph databases and explain why they are such a valuable tool for businesses.
What Is A Graph Database?
The database is a NoSQL database that uses graphs to store data. In other words, it stores information as nodes and edges instead of tables and rows. It makes it an ideal tool for storing and analyzing data that can be represented as a network, such as social media data or the connections between servers in a data center.
Types of Graph Databases
1) Property Graphs
Property graphs are the most common type of graph database. They store data as nodes, edges, and properties.
Nodes represent entities, such as people or businesses. Edges represent the relationships between nodes.
Properties are key-value pairs that describe nodes and edges.
For example, a node representing a person might have a name, age, and location properties. An edge representing the relationship between two people might have a property such as a date met.
They are also easy to query, making them a good choice for applications that quickly find specific data.
Hypergraphs are similar to property graphs, but they allow for more complex relationships between nodes. In a hypergraph, each node can be connected to any number of other nodes, and there is no limit on the connections that a node can have. Hypergraphs are well-suited for applications that require complex relationships between data elements.
They also help with data mining applications, where the goal is to find patterns in the data. By analyzing the structure of the hypergraph, it is possible to find relationships that would be difficult to detect in a property graph.
A triplestore is a database that stores data in the form of graphs. Triples are entities and their relationships, represented as a subject-predicate-object triple. Graphs can be used to model data, including social networks, knowledge graphs, and product catalogs.
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Most triplestores are open source and can be used for free. They are typically used to store large amounts of data, making them suitable for big data applications. Some popular triplestores include NeoGraphs, Stardog, and AllegroGraph.
A multi-model database supports many data models. This type of database is designed to handle a variety of workloads, including OLTP, OLAP, and big data applications. Multi-model databases are often used in organizations with a mix of relational and non-relational data.
OLTP means online transaction processing. This type of workload is characterized by a large number of small transactions, such as those that occur when a user purchases from an e-commerce site.
OLAP refers to online analytical processing. Here, the focus is on running complex queries against large data sets.
Big data applications involve processing vast volumes of data in a short amount of time.
So, now you know what is a graph database. Graphs are the best way to represent data when entities are interconnected. If your business is looking for an easy way to store and analyze data, a graph database is the solution you need.