Introduction

There are several types of databases in the field of databases that are made to store and manage data in various ways. Relational databases and graph databases are two common varieties of databases. Although both types of databases are effective for storing and retrieving data, there are significant variations between them in terms of their design, functionality, and intended applications.

The relational model, which arranges data into tables with rows and columns, is the foundation of relational databases, which have been around for decades. The associations between entities are formed by means of keys or foreign keys, and each table represents an entity or an object. Relational databases' principal language for data management and querying is Structured Query Language (SQL). MySQL, PostgreSQL, and other well-known relational databases are some examples.

Graph databases, on the other hand, are a more recent variety of database that store data as nodes and edges in a graph structure. Edges indicate the connections between nodes, while nodes' nodes represent entities or objects. In order to search and retrieve data from graph databases, users can utilise graph-based querying languages like Cypher or Gremlin. Neo4j, Amazon Neptune, and OrientDB are a few well-known graph databases.

Structure

Relational databases and graph databases differ significantly in terms of their structures. Relational databases use tables with fixed columns and rows to hold data, and keys or foreign keys are used to build associations between entities. The data that can be represented in a tabular format with predetermined columns and relationships is appropriate for this structure. As opposed to traditional databases, graph databases employ an adaptable graph structure that supports dynamic interactions between elements. As a result, graph databases are more suited for data with intricate and dynamic interactions, such as that found in social networks, recommendation engines, or fraud detection.

Functionality

Relational databases are excellent at handling structured data with well-defined schemas and are efficient at maintaining a lot of transactional data. They are perfect for applications like online banking or e-commerce.

Whereas, graph databases are excellent at handling linked data with changing relationships. They are perfect for applications that call for in-depth understanding of relationships, including social media analysis, recommendation engines, or fraud detection, because they are built to effectively represent and traverse complicated relationships between entities. Real-time applications can benefit from graph databases' quick and scalable graph-based queries, which enable real-time traversal of expansive graphs.

Where Are They Used

If you have a well defined schema and have a good idea of what your transactions and data querying looks like, it is probably a good idea to go with a relational database.

However, if your data has complex relationships e.g. social networks or user data based on recommendations, it might be better to use a graph database.

Apache AGE and its capabilities

Apache AGE® is a PostgreSQL extension that provides graph database functionality.

Apache AGE serves to provide graph data processing capabilities to all relational databases, mainly PostgreSQL.

By using Apache AGE, users can use graph algorithms and data-retrieving techniques on a relational database. AGE allows you to make the best of both worlds.

Conclusion

Graph databases and relational databases are different to each other for a lot of reasons, some of which we have discussed. They both serve distinct and important use cases and the choice to use one over the other comes straightly from the nature of the data.

Contribute to Apache AGE
Apache AGE website: https://age.apache.org/

Apache AGE Github: https://github.com/apache/age