The Most Popular Graph Database, Ranked
Voting rules: Choose the graph database you think is the most popular!
Updated on Apr 24, 2024 07:13
In the dynamic world of data management, where relationships and connections between data points can be as crucial as the data itself, graph databases have grown significantly in importance. They are employed in various industries to provide deeper insights and stronger data interconnectivity. Having a cohesive ranking of popular graph databases can assist developers and businesses in choosing the right tool that matches their specific needs and requirements.
This interactive list responds to real-world usage and feedback, continually updating based on your contributions. By participating, you not only gain insight into which databases are preferred by peers but also influence the ranking directly. Your vote helps ensure that the list remains current and reflective of the community's consensus on what works best for different scenarios.
What Is the Most Popular Graph Database?
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162votesNeo4j is the most popular graph database, it is widely used in various industries such as finance, healthcare, and social media. It is an open-source graph database that offers high performance, scalability, and flexibility. Neo4j is known for its powerful Cypher query language, which allows users to query and manipulate data in a graph.Neo4j is a highly popular graph database that is designed to store, query, and analyze vast amounts of connected data. It provides a flexible and efficient way to model, manage, and traverse complex relationships between data points. Neo4j is widely used in various industries, including finance, healthcare, retail, and social media.
- Cypher Query Language: A declarative query language for querying and manipulating graph data
- ACID Compliance: Ensures data consistency and reliability
- Scalability: Supports large-scale graphs with billions of nodes and relationships
- High Performance: Optimized for fast traversal and retrieval of connected data
- Native Graph Processing: Provides efficient native graph storage and processing
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239votes
Amazon Neptune
Amazon Web Services (AWS)Amazon Neptune is a fully managed graph database service that is built for the cloud. It is scalable, highly available, and offers fast performance. Amazon Neptune is designed to handle large-scale graph data, making it an ideal choice for businesses that need to store and analyze complex relationships between entities.Amazon Neptune is a fully managed graph database service provided by Amazon Web Services (AWS). It is designed to store, query, and analyze highly connected data with billions of relationships. Neptune is a popular choice for applications that require querying relationships and patterns, such as social networking, recommendation engines, fraud detection, and knowledge graphs.- Data Model: Labeled Property Graph
- Query Language: Apache TinkerPop Gremlin
- Scalability: Highly scalable, supports up to billions of relationships
- Durability: ACID-compliant with multiple copies of data stored across multiple Availability Zones
- Auto-scaling: Automatically scales up or down to handle changes in workload
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325votes
Microsoft Azure Cosmos DB
MicrosoftAzure Cosmos DB is a multi-model database service that supports graph, document, key-value, and column-family data models. It is highly scalable and offers low latency and high throughput. Azure Cosmos DB is a popular choice for enterprises that need to manage large volumes of data across multiple regions.Microsoft Azure Cosmos DB is a globally distributed, multi-model database service built for scalable and highly available applications. It allows you to elastically scale throughput and storage across any number of geographic regions while guaranteeing low latency and high availability. It supports multiple data models including document, key-value, column-family, and graph models, making it a versatile choice for various application requirements. With its global distribution, multi-model support, and comprehensive SLAs, Azure Cosmos DB simplifies development and enables developers to focus on building their applications.- Scalability: Allows elastic scaling of throughput and storage across any number of geographic regions.
- Global Distribution: Enables data replication and low-latency access across multiple regions.
- Multi-Model Support: Supports multiple data models including document, key-value, column-family, and graph models.
- High Availability: Provides high availability with guaranteed SLAs for uptime and performance.
- Low Latency: Ensures minimal delay in accessing and querying data.
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421votesOrientDB is an open-source graph database that combines the features of a graph database and a document database. It is designed to be highly scalable and offers fast performance. OrientDB is used in various industries such as healthcare, finance, and social media.OrientDB is a popular open-source graph database management system that combines the features of a document database and a graph database. It was designed by Luca Garulli.
- Flexible Schema: Allows schema-less or schema-full data modeling.
- ACID Transactions: Supports Atomicity, Consistency, Isolation, and Durability.
- Multi-Language Support: Provides binding APIs for Java, Python, JavaScript, and more.
- Massive Scalability: Capable of handling large datasets and distributed deployments.
- SQL-Like Query Language: Offers a powerful and intuitive query language inspired by SQL.
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511votesJanusGraph is an open-source distributed graph database that is designed to handle large-scale graph data. It is built on top of Apache TinkerPop, which is a graph computing framework. JanusGraph is used in various industries such as healthcare, finance, and social media.JanusGraph is a highly scalable, distributed graph database optimized for querying large graph datasets. It is designed to efficiently handle complex graph traversals and support highly concurrent workloads. JanusGraph is based on the BigTable data model and supports ACID transactions.
- Scalability: JanusGraph is designed to scale horizontally and is capable of handling massive graphs with billions of vertices and edges.
- Distributed Architecture: JanusGraph is built with a distributed architecture that enables high availability, fault tolerance, and load balancing across multiple machines.
- Support for Various Storage Backends: JanusGraph supports multiple storage backends, including Apache Cassandra, Apache HBase, Google Cloud Bigtable, Oracle BerkeleyDB, and more.
- Flexible Data Model: It provides a flexible property graph data model that allows users to define custom vertex and edge labels, properties, and indexes.
- Query Optimization: JanusGraph optimizes graph traversals with a query planner and executor, minimizing latency, and enabling efficient execution of complex graph queries.
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67votesArangoDB is a multi-model database that supports graph, document, and key-value data models. It is designed to be highly scalable and offers fast performance. ArangoDB is used in various industries such as healthcare, finance, and social media.ArangoDB is a popular multi-model database that provides a combination of key-value, document, and graph data models. It is designed for high-performance, flexible, and distributed data management.
- Data Models: Key-Value, Document, Graph
- Architecture: Native multi-model
- Language Support: JavaScript, AQL (ArangoDB Query Language)
- Scalability: Horizontally scalable
- Replication: Asynchronous replication
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71votesTigerGraph is a graph database that is designed to handle large-scale graph data. It is known for its high performance and scalability. TigerGraph is used in various industries such as healthcare, finance, and social media.TigerGraph is a highly scalable and fast graph database designed for processing and analyzing complex, connected data. It is known for its advanced graph processing capabilities and real-time analytics. TigerGraph allows users to create and execute complex graph queries with high performance and agility.
- Scalability: TigerGraph can handle large datasets and scale horizontally to support growing data needs.
- Performance: TigerGraph offers exceptional speed and efficiency in processing complex graph queries.
- Parallel Processing: TigerGraph distributes computations across multiple machines to enable parallel processing and faster query execution.
- Real-time Analytics: TigerGraph supports real-time analytics on connected data, enabling users to derive valuable insights as new data arrives.
- Graph Algorithms: TigerGraph provides a wide range of built-in graph algorithms for analyzing and traversing complex graphs efficiently.
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88votesStardog is a graph database that is designed to store and manage RDF data. It is used in various industries such as healthcare, finance, and social media. Stardog is known for its powerful query language and its ability to handle complex relationships between entities.Stardog is a popular graph database that combines RDF and SPARQL engine with scalable, enterprise-grade features. It provides a knowledge graph platform for storing, querying, and reasoning over interconnected data.
- Scalability: Stardog can handle large-scale graphs with billions of triples.
- Query Language: It supports SPARQL, a powerful query language for querying RDF datasets.
- Reasoning: Stardog supports various reasoning capabilities, including rule-based and schema-based reasoning.
- Virtual Graphs: It allows federated querying across different data sources, including external databases and web APIs.
- Transaction Support: Stardog provides ACID-compliant transactions, ensuring consistency and reliability.
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96votesVirtuoso is a graph database that is designed to store and manage RDF data. It is used in various industries such as healthcare, finance, and social media. Virtuoso is known for its support for SPARQL, which is a query language for RDF data.Virtuoso is a popular graph database that offers efficient storage and retrieval of graph-structured data. It provides advanced features and capabilities for working with large-scale graphs, making it a preferred choice for various applications.
- Scalability: Virtuoso can handle massive graphs with billions of nodes and edges, enabling efficient data storage and retrieval.
- Query Language Support: It supports various query languages such as SPARQL, SQL, and RDF.
- Native RDF Triple Store: Virtuoso includes a native RDF triple store that allows efficient storage and querying of RDF data.
- Graph Visualization: It offers built-in graph visualization tools for exploring and analyzing graph data visually.
- SPARQL Endpoint: Virtuoso provides a SPARQL endpoint for executing SPARQL queries over the stored graph data.
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107votesGraphDB is a graph database that is designed to store and manage RDF data. It is used in various industries such as healthcare, finance, and social media. GraphDB is known for its support for RDF and OWL, which are standards for representing and querying data.GraphDB is a highly-scalable, enterprise-ready graph database that allows users to unlock the power of connected data. It combines advanced query optimization techniques with a native RDF storage and SPARQL query engine, enabling efficient management and exploration of graph data.
- Scalability: Supports millions to billions of nodes and relationships
- Performance: Optimized for fast query execution and traversal of large and complex graphs
- ACID Compliance: Ensures data consistency and transactional integrity
- High Availability: Built-in replication and clustering to ensure fault-tolerance and continuous operation
- Security: Offers data access control mechanisms to safeguard sensitive information
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Discussion
Ranking factors for popular graph database
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Adoption and Market ShareConsider the popularity and market share of the graph database, as more users generally indicate a better and more reliable technology.
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Performance and ScalabilityEvaluate the graph database's ability to handle large volumes of data, high query loads, and growing data sizes.
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Query LanguageAssess the expressiveness, ease of use, and capabilities of the query language provided by the graph database.
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Data Model FlexibilityExamine the graph database's ability to support various data models, including property graphs, RDFgraphs, and hypergraphs.
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Extensibility and InteroperabilityDetermine whether the graph database can interface with other existing technology stacks, APIs, and storage formats.
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Ecosystem and Community SupportAnalyze the availability of tools, libraries, and plug-ins built around the graph database, as well as the level of community involvement through forums, Slack channels, and mailing lists.
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Developer Resources and DocumentationInvestigate the availability of comprehensive documentation, tutorials, and other learning materials to help users better understand and utilize the graph database.
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Security FeaturesEvaluate the graph database's built-in security mechanisms, such as access control, encryption, and audit logging, to ensure data protection and compliance with regulations.
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High Availability and Disaster RecoveryAssess the graph database's ability to handle hardware failures, node failures, and network partitions to maintain seamless data operations.
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Vendor Support and Commercial ServicesConsider the level of official support, consulting, and professional services provided by the graph database vendor to help companies with implementation, integration, and optimization.
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Licensing and CostCompare the licensing models, such as open-source, free, and commercial, as well as the cost of yearly subscriptions or per-seat charges, to choose a graph database that aligns with your budget.
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
- 2174 views
- 188 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.
More information on most popular graph database
Graph databases have become increasingly popular in recent years due to their ability to handle complex data relationships. Unlike traditional relational databases, graph databases store data as nodes and edges, allowing for more efficient querying and analysis of data.
One of the most popular types of graph databases is the property graph model, which stores data as nodes with properties (such as name, age, or location) and edges that represent relationships between nodes. These relationships can be directional or bidirectional, and can carry additional properties as well.
Some of the most well-known graph databases include Neo4j, OrientDB, and Apache TinkerPop. Each of these databases has its own unique features and strengths, making them suitable for different use cases.
As the demand for more efficient and effective data management continues to grow, it is expected that the popularity of graph databases will only continue to rise.
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