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Graph Databases vs Relational Databases: Key Differences

Graph Databases vs Relational Databases: Key Differences

It uses Cypher for querying and managing graph data.

Common applications include recommendation systems, fraud detection, knowledge graphs, and network analysis.

Amazon Neptune

Amazon Neptune is a managed graph database service offered by AWS.

It supports property graph and RDF graph models. Applications can use query languages such as Gremlin, openCypher, and SPARQL, depending on the chosen model and configuration.

It can be useful for organisations building graph-based applications within AWS.

TigerGraph

TigerGraph is designed for large-scale graph analytics and connected data applications.

It supports graph-based analysis for areas such as fraud detection, recommendations, and customer intelligence.

ArangoDB

ArangoDB supports multiple data models, including graph, document, and key-value data.

This allows developers to manage different types of information within one database platform.

JanusGraph

JanusGraph is an open-source, distributed graph database designed for large graphs.

It can work with different storage and indexing backends, depending on the deployment architecture.

Before selecting a platform, review its query support, scaling options, operational requirements, licensing, and integration capabilities.

How to Choose the Right Database for Your Business

Selecting a database requires more than comparing technical features.

The right choice depends on your application's structure, query patterns, development resources, and future requirements.

Here are the main factors to review.

1. Understand your data: Determine whether your information is mainly structured records or highly connected entities.

2. Review application queries: Identify whether your system needs traditional SQL operations or complex relationship traversal.

3. Check transaction requirements: Financial and business applications may need strong transactional guarantees.

4. Evaluate scalability: Consider expected data growth, traffic, query volume, and infrastructure requirements.

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