Graph databases are powerful tools for managing connected data and running complex queries across relationships. But with so many options, how do you choose the right graph database for your needs? This guide examines the top 10 graph database solutions to consider in 2023 based on features, capabilities, and ease of use.
What we cover
A graph database stores data in a graph structure with nodes, edges, and properties. It allows you to efficiently query relationships between entities. Graph databases are optimized for handling highly interconnected data and providing insights through data relationships. They differ from traditional relational databases which are not as adept at mapping connections.
Graph databases provide a powerful way to manage and query highly connected data. At their core, graph databases represent entities as nodes and the relationships between them as edges. This allows efficiently traversing relationships to uncover valuable insights.
Graph databases are important for enterprises that need to map relationships across massive datasets. Common use cases include social networking, fraud detection, real-time recommendations, and network and IT operations. They can map relationships across massive, complex domains and provide real-time analytics not possible in relational databases.
We recommend considering a graph database when you need to optimize for relationship-based queries and connections between data. Graph databases simplify modeling networked data domains and make traversing relationships highly performant. They empower users to ask complex questions not feasible using traditional relational databases.
There are several key benefits that graph databases provide over other database models:
- Flexible Data Modeling: Easily map complex relationships without restrictive schema.
- High Performance Queries: Rapidly traverse relationships in the graph for fast query results.
- Real-Time Insights: Conduct real-time analytics of data connections and patterns.
- Data Agility: Easily evolve data model without migrations.
- Visualization: Native graph data model provides intuitive visualization.
Key factors to consider when choosing a graph database include:
- Query Performance: Speed for traversing complex graph queries.
- Scalability: Ability to scale graph capacity and performance as data volumes grow.
- Data Model Flexibility: Support for varied data structures and relationships.
- Developer Experience: Quality of documentation, APIs, drivers and tooling.
- Visualization: Graph visualization and exploration capabilities.
- ACID Compliance: Support for atomicity, consistency, isolation and durability.
- High Availability: Resilience and uptime service level agreements.
- Cloud Services: Managed cloud services options.
- Community & Support: Active user community and responsive technical support.
Graph database software costs range widely from free open source options to enterprise commercial licenses costing over $25,000 per year. Pricing is typically based on the size of the database, number of nodes, relationships, and features. Managed cloud services often charge based on resource utilization. Free developer licenses are common.
|Criteria||Neo4j||OrientDB||DGraph||DataStax||TigerGraph||Titan||ArangoDB||Stardog||Redis Graph||Amazon Neptune|
|Query Language||Cypher||SQL, Gremlin||DQL||Gremlin||GSQL||Gremlin||AQL||SPARQL||Redisgraph||OpenCypher|
|Data Model Flexibility||✓✓✓||✓✓||✓✓✓||✓✓✓||✓✓✓||✓✓||✓✓✓||✓✓✓||✓✓||✓✓✓|
|Performance at Scale||✓✓✓||✓✓||✓✓||✓✓✓||✓✓✓||✓✓||✓✓||✓✓||✓||✓✓|
|Tooling & Visualization||✓✓✓||✓✓||✓||✓✓||✓✓✓||✓||✓✓||✓✓||✓||✓|
|Cloud Managed Service||✓||✓||✓||✓||✓||✓||✓||✓✓✓|
Neo4j is one of the most popular native graph databases used by enterprises worldwide. It is ACID compliant with advanced performance scaling and clustering capabilities.
- Key Features: Cypher query language, graph algorithms, clustering, visualization
- Pricing: Free community edition. $25,000+ per year for enterprise edition.
- Reviews: 4.4/5 stars on G2 Crowd based on 153 reviews.
- Website: https://neo4j.com
Neo4j is the market leading native graph database, known for its Cypher query language, developer experience and performance at scale. It provides native clustering, elastic scalability and advanced security features. Neo4j supports complex graph traversals and analytics through Cypher along with graph visualization. The developer experience is excellent with Neo4j Drivers for most languages. Neo4j Bloom provides a visual graph explorer. Both community and enterprise editions are available.
- Cypher query language
- Graph algorithms and analytics
- Visual graph explorer
- Horizontal scaling and clustering
- ACID compliance
Neo4j is the most robust and widely-adopted native graph database. It offers excellent performance, scalability, and security along with a great developer experience. For graph-intensive applications that require relationship analysis, Neo4j is a top choice.
OrientDB is an open source multi-model graph database that supports document and key-value features. It uses both SQL and graph querying.
- Key Features: SQL, ACID Transactions, Multi-model
- Pricing: Free open source. Commercial licenses $990-$9990 per year.
- Reviews: 4.3/5 stars on Capterra based on 24 reviews.
- Website: https://orientdb.org
OrientDB is one of the most versatile open source graph databases, combining the flexibility of a document database with the relationship modeling of a graph database. It supports both SQL and graph query languages. OrientDB can handle complex graph traversals and relationships efficiently. It features automatic sharding and multi-master replication for performance at scale. The community edition is free with commercial licenses available.
- SQL and Graph query languages
- Automatic sharding
- Multi-model: Graph, Document, Key-Value
- ACID transactions
OrientDB offers great flexibility as a multi-model graph database with SQL capabilities. It works well for both connected graph data and structured document data.
Dgraph is a horizontally-scalable open source graph database designed for distributed environments and large datasets. It uses the GraphQL-based Dgraph Query Language (DQL).
- Key Features: Horizontal scaling, Cloud native, DQL
- Pricing: Free open source. From $3/month managed cloud.
- Reviews: 4.7/5 stars on G2 Crowd based on 15 reviews.
- Website: https://dgraph.io
Dgraph is optimized for lightning fast queries across distributed data clusters. It is highly scalable to manage massive graphs and complex traversals. Dgraph supports GraphQL schemas and queries through DQL. It also provides read-only global consensus via Ratel for strong consistency across clusters. Dgraph Cloud provides a fully-managed DBaaS. The open source community edition is also available.
- Horizontally scalable
- Dgraph Query Language (DQL)
- Strongly consistent distributed clusters
- Optimized for complex traversals
For large-scale graph applications that require low latency queries across distributed data, Dgraph is a top choice. It works well for global scale graph databases.
DataStax Enterprise Graph is a distributed graph database built on top of DataStax’s Apache Cassandra NoSQL database. It uses Gremlin as the query language.
- Key Features: Gremlin, Distributed, Cassandra-based
- Pricing: Free developer version. $19,350 per 4 node cluster per year.
- Reviews: 4.4/5 stars on G2 Crowd based on 20 reviews.
- Website: https://www.datastax.com
DataStax Enterprise Graph provides a Cassandra-based graph platform designed for enterprise scale and performance. It leverages the distributed scale-out architecture of Cassandra combined with integrated graph capabilities. This enables massive graphs with low latency traversals and analytics. It provides production-ready capabilities including analytics, search, and visualization out of the box.
- Distributed graph database on Cassandra
- Gremlin graph query language
- Integrated analytics and search
- Visualization with DataStax Studio
For companies already using Cassandra that need to add enterprise-scale graph capabilities, DataStax Enterprise Graph is the natural fit.
TigerGraph provides a distributed, parallel graph database with real-time analytics capabilities. It uses TigerGraph Query Language (GSQL).
- Key Features: Graph parallel analytics, GSQL, Visual Explorer
- Pricing: Free developer edition. $7,999+ per year for standard edition.
- Reviews: 4.4/5 stars on Gartner Peer Insights based on 22 reviews.
- Website: https://www.tigergraph.com
TigerGraph excels at high performance graph analytics across parallel graph clusters. Its distributed architecture handles complex graph workloads with blazing fast query speeds. Advanced graph algorithms provide real-time insights. TigerGraph Cloud offers a fully managed DBaaS. It also provides standards support for GSQL, GRPC, and ODBC.
- Graph parallel analytics
- GSQL query language
- Distributed native architecture
- Visual Explorer for visualization
For graph analytics-intensive use cases that require real-time insights, TigerGraph provides industry-leading capabilities.
Titan is an open source distributed graph database optimized for storing and querying large graphs. It is a transactional database that supports ACID guarantees.
- Key Features: Scalable, ACID Transactions, Pure Java
- Pricing: 100% open source and free
- Reviews: 4.5/5 stars on Capterra based on 2 reviews.
- Website: https://titan.thinkaurelius.com/
Titan is a Java-based open source graph database that provides transactional support, schema management, and superior performance at scale. It can store graphs containing hundreds of billions of vertices and edges distributed across clusters. Titan is highly extensible and integrates with popular big data stacks like Hadoop and Spark. For organizations that want a robust graph database with enterprise features for free, Titan is a great choice.
- Distributed ACID graph database
- Highly scalable and available
- Gremlin query language support
- Hadoop and Spark integrations
For developers that want a feature-rich graph database that can scale and is free to use, Titan is hard to beat. It works for both graph and big data applications.
ArangoDB is a multi-model open source database that supports graphs, documents, and search. It provides high availability and ACID transactions.
- Key Features: Multi-Model, JIT queries, Foxx Microservices
- Pricing: Open source free. $2,940 per year starter license.
- Reviews: 4.4/5 stars on G2 Crowd based on 18 reviews.
- Website: https://www.arangodb.com
ArangoDB distinguishes itself by being a multi-model database that natively combines the flexibility of documents and graphs. It allows developers to use the best database model for specific use cases. ArangoDB provides a SQL-like query language AQL, auto-sharding, cluster replication, and impressive performance. The built-in Foxx framework allows building microservices.
- Multi-model: Graph, Document, Search
- AQL query language
- ACID transactions
- Foxx Microservices
For companies that need multiple data models, ArangoDB allows seamlessly combining graphs, documents, and search in a single database.
Stardog provides an enterprise knowledge graph platform with high performance graph analytics. It uses SPARQL as the query language.
- Key Features: Knowledge Graphs, High Performance, SPARQL
- Pricing: $5,000 per year for enterprise license.
- Reviews: 4.7/5 stars on G2 Crowd based on 6 reviews.
- Website: https://www.stardog.com
Stardog is tailored for Knowledge Graph applications with support for massive data sets and real-time analytics. It provides native support for RDF and OWL standards. Stardog utilizes database virtualization to access data from disparate sources. It’s highly scalable and provides excellent performance. The Stardog Gra.ph visualizer allows exploring connections in the knowledge graph.
- Knowledge graph specialized
- SPARQL query language
- High performance analytics
- Database virtualization
For Knowledge Graph applications that require real-time analytics over massive interconnected data, Stardog is purpose-built solution.
Redis Graph is a graph database module for Redis that allows storing property graphs and running queries through the Cypher-compatible RedisGraph query language.
- Key Features: Cypher support, memory-first, Redis module
- Pricing: 100% open source free
- Reviews: 4.7/5 stars on Capterra based on 6 reviews.
- Website: https://redis.io/docs/stack/graph/
Redis Graph provides graph database capabilities as a seamless module that works with Redis for a superfast, in-memory graph store. It supports both directed and undirected property graphs along with multiple graph models. Cypher query language is available along with other constructs. Redis Graph is great for accelerating real-time recommendations and relationship analysis at scale.
- Cypher query language
- In-memory graph performance
- Multiple graph models
- Part of Redis ecosystem
For high speed graph capabilities using Redis, Redis Graph provides excellent graph performance and Cypher support as a module.
Amazon Neptune is a fully managed graph database service available as part of Amazon Web Services (AWS). It supports both Gremlin and SPARQL query languages.
- Key Features: Fully managed, high availability, Gremlin & SPARQL
- Pricing: From $0.08 per node hour (~$57 per month for dc2.large)
- Reviews: 4.1/5 stars on G2 Crowd based on 7 reviews.
- Website: https://aws.amazon.com/neptune/
Amazon Neptune simplifies running graph workloads by providing Neptune as a fully managed service. It handles provisioning, scaling, security and high availability. You can run queries using both SPARQL and Gremlin graph query languages. It provides high performance at scale and integration with visualization and analytics services on AWS.
- Fully managed graph DBaaS
- High availability built-in
- Gremlin & SPARQL support
- Integrates with AWS analytics
For companies using AWS that want a resilient, managed graph database with minimal operational overhead, Neptune is a great choice.
What are the main use cases for graph databases?
Some of the top use cases are social networking, fraud detection, real-time recommendation engines, master data management, network and IT operations, customer 360 profiles, and supply chain management.
What query languages do graph databases use?
Common graph database query languages include Cypher, Gremlin, SPARQL, and Dgraph Query Language (DQL). Some graph databases also support SQL.
How do graph databases handle scalability and performance?
Graph databases use database sharding, parallel distributed architectures, and graph partitioning techniques to provide horizontal scalability. This allows performance to remain high even as graph dataset size increases significantly.
Can graph databases integrate with existing frameworks and tools?
Yes, graph databases provide APIs and drivers for integration with popular programming languages, visualization tools, cloud services, and data science stacks.
What type of data will be stored in the graph database?
Graphs store two primary data objects – nodes (vertices) and edges (relationships). Nodes represent entities like customers or products. Edges represent the relationships between nodes like purchases, friendships or follows.
Graph databases provide powerful solutions for working with highly connected data and uncovering essential insights from complex relationships. If you’re considering a graph database, the options reviewed in this guide represent the top solutions to evaluate based on features, capabilities, and ease of use. Focus on options that align with your technical environment, development resources, and application requirements.