The Most Popular Graph Database, Ranked

Choose the graph database you think is the most popular!

Author: Gregor Krambs
Updated on May 18, 2024 06:40
Choosing the right graph database is crucial for developers who deal with complex data relationships and structures in their projects. A well-chosen graph database improves query performance, offers efficient data connectivity, and scales seamlessly along with growing data needs. As various options vary in features, ease of use, and performance, selecting the best can be challenging. This space allows users to cast votes for the graph databases they prefer, helping to clarify which are currently leading in popularity and satisfaction. Such rankings can guide newcomers towards making informed decisions and offer insights into industry trends. Whether you're a seasoned developer or just starting, your vote contributes to a broader understanding of which tools are most valued in the tech community.

What Is the Most Popular Graph Database?

  1. 1
    62
    votes

    Neo4j

    Neo4j is a highly popular graph database known for its performance and scalability, often used for social graphs, recommendation engines, and fraud detection.
    • Model: Property graph
    • Query Language: Cypher
  2. 2
    39
    votes

    Amazon Neptune

    Amazon Neptune is a fully managed graph database service, optimized for storing billions of relationships and querying the graph with milliseconds latency.
    • Model: Property graph and RDF
    • Query Languages: Gremlin, SPARQL
  3. 3
    25
    votes

    Microsoft Azure Cosmos DB

    Azure Cosmos DB is a globally distributed, multi-model database service that supports graph processing through the Gremlin API, offering turnkey global distribution and horizontal scale.
    • Model: Multi-model (Graph, Document, Key/Value, Column-family)
    • Query Language: SQL (Cosmos DB SQL), Gremlin
  4. 4
    21
    votes

    OrientDB

    OrientDB is a multi-model database that supports graph, document, object, and key/value data models. It is known for its flexibility and scalability.
    • Model: Multi-model (Graph, Document, Object, Key/Value)
    • Query Language: SQL-like
  5. 5
    11
    votes

    JanusGraph

    JanusGraph is an open-source, distributed graph database designed to support the processing of large graphs. It is built on top of Apache TinkerPop.
    • Model: Property graph
    • Query Language: Gremlin
  6. 6
    7
    votes

    ArangoDB

    ArangoDB is a multi-model database that supports graph, document, and key/value data models, making it versatile for a wide range of applications.
    • Model: Multi-model (Graph, Document, Key/Value)
    • Query Language: AQL (ArangoDB Query Language)
  7. 7
    0
    votes

    Titan

    Titan is a scalable graph database optimized for storing and querying graphs containing billions of vertices and edges distributed across a multi-machine cluster.
    • Model: Property graph
    • Query Language: Gremlin
  8. 8
    0
    votes

    AllegroGraph

    AllegroGraph is a high-performance, scalable graph database designed to store RDF data and support SPARQL, RDFS++, and Prolog reasoning from within a single database.
    • Model: RDF
    • Query Language: SPARQL
  9. 9
    0
    votes

    DataStax Enterprise Graph

    DataStax Enterprise Graph is a scalable real-time graph database, built on Apache Cassandra, designed for enterprises needing to manage complex and dynamic data relationships.
    • Model: Property graph
    • Query Language: Gremlin
  10. 10
    0
    votes

    Blazegraph

    Blazegraph is a scalable, high-performance graph database supporting Blueprints and RDF/SPARQL APIs. It is known for its use in the Wikimedia Foundation's Wikidata query service.
    • Model: RDF
    • Query Language: SPARQL

Missing your favorite graph database?

Graphs
Error: Failed to render graph
Discussion
No discussion started, be the first!

About this ranking

This is a community-based ranking of the most popular graph database. We do our best to provide fair voting, but it is not intended to be exhaustive. So if you notice something or Graph database is missing, feel free to help improve the ranking!

Statistics

  • 2286 views
  • 165 votes
  • 10 ranked items

Voting Rules

A participant may cast an up or down vote for each Graph database once every 24 hours. The rank of each Graph database is then calculated from the weighted sum of all up and down votes.

Additional Information

More about the Most Popular Graph Database

Neo4j
Rank #1 for the most popular graph database: Neo4j (Source)
Graph databases have become popular in recent years. They store data in nodes and edges, which makes them different from traditional databases. Nodes represent entities such as people, products, or events. Edges show the relationships between these entities. This structure makes it easy to map and query complex networks.

Graph databases excel in scenarios where relationships are key. Social networks, recommendation engines, and fraud detection systems benefit from their use. In these applications, the connections between data points are as important as the data points themselves. Traditional databases struggle with these tasks because they rely on tables. Tables can become complex and slow when handling many relationships.

The rise of big data has fueled interest in graph databases. They handle large volumes of interconnected data with ease. Companies need to analyze customer behavior, detect patterns, and predict trends. Graph databases provide the tools to do this efficiently. They allow for quick queries and real-time insights, which are crucial for decision-making.

The flexibility of graph databases also appeals to developers. They can add new types of relationships without restructuring the entire database. This adaptability speeds up development and reduces costs. It also means that businesses can respond faster to changes in their environment.

Query languages for graph databases are intuitive and powerful. They allow users to express complex queries in a simple way. These languages focus on the relationships between data points. This focus makes it easier to retrieve meaningful information from the database.

Graph databases support various use cases. In healthcare, they help track patient records and medical histories. In logistics, they optimize routes and manage supply chains. In cybersecurity, they identify and mitigate threats. The ability to visualize data as a graph makes it easier to understand and act upon.

Performance is another advantage. Graph databases can traverse millions of relationships in milliseconds. This speed is vital for real-time applications. It ensures that users get the information they need without delay. This performance edge is one reason why many companies are adopting graph databases.

Scalability is also a key feature. Graph databases can scale horizontally and vertically. They can handle increasing amounts of data without a drop in performance. This scalability makes them suitable for growing businesses and large-scale operations.

The community around graph databases is vibrant and supportive. Many resources are available for learning and troubleshooting. Open-source options provide a cost-effective way to get started. Commercial solutions offer advanced features and support for enterprise needs.

Despite their benefits, graph databases are not a silver bullet. They are best suited for specific scenarios. For simple, flat data, traditional databases may still be the best choice. Understanding the strengths and limitations of graph databases is crucial for making the right decision.

The future of graph databases looks promising. As data continues to grow and become more interconnected, the need for efficient data management will increase. Graph databases are well-positioned to meet this need. They offer a unique approach to handling complex data and relationships. This makes them an essential tool in the modern data landscape.

Share this article