Databases play an essential role in organizations, letting you store data in a structured manner. Some of the different types of databases are: centralized, hierarchical, key-value, and distributed.
However, they all have several limitations. For instance, relational databases store details in the form of tables and columns. Although they let you identify the relationships, they do not provide a deeper insight which would be more beneficial.
Graph databases fill that gap efficiently, letting you understand the personal relationships between data items. They are efficient and quick and let you handle large volumes of information without difficulty.
But what is a graph database, and how does it work? How can it help you, and why should you use it? Get the answers to these kinds of questions below.
How does it work?
This type of database stores information in graphs with nodes, edges, and properties to help you identify their relationships. A single node can have different kinds of relationships.
It makes relationship identification quick and convenient because they are contained in the database. Other databases calculate them only at query times, which are slower and will cost you time.
How is it different from other databases?
The emphasis is on relationships between the items in databases that store data in graph structures. Other hierarchical, object-oriented, key-value, and wide-column databases emphasize data storage as tables and documents.
A graph data structure is also different from relational databases, despite some people mistaking it for the same thing. Although relational databases also focus on relationships, they store the information poorly.
If you stored data in a traditional tabular dataset with rows and structures, you would get limited insights about the entries. You would not find them helpful for addressing interconnected questions.
In which industries is it helpful?
Companies use this structure for various reasons, including fraud detection, knowledge graphs, master data management, network, and IT operations, and social networking. Telecommunications, government, life sciences, retail and financial services are industries that utilize it the most.
Take the example of fraud detection, for instance. Most traditional fault detection techniques focus on data points like IP addresses, devices, or specific accounts. However, these don’t help identify the fraudsters who form fraud rings.
Database platforms like neo4j look beyond the individual data points to the connections between the fraud activities. It helps agencies identify patterns that are difficult to detect with traditional relational databases.
What are the benefits of using graph databases?
It has plenty of benefits, and here are two of them.
Databases that store data as graph structures work best for handling data relationships effectively. Traditional data structures suffer from scalability problems, as they cannot run queries as the number of relationships increase.
Further, it is the best way of looking for data because each vertex stores information about its surrounding vertices (a set of points). You won’t have difficulty searching for a specific query even when the data size increases.
It offers excellent flexibility
Data teams can add or alter the existing graph structure with increased storage without affecting its current functionality. It saves them from the hassle of modeling a domain beforehand to adjust to the increased load.
Most databases, like relational and hierarchical, work on a pre-fixed model. Graph databases give you the option to store data flexibly, giving them more agility.
These are some things related to the question: what is a graph database? It has many benefits, from high performance to establishing exact relationships between data entries.