# Best Graph Database Solutions

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Graph databases use topographical data models to store data. These databases connect specific data points (nodes) and create relationships (edges) in the form of graphs that can then be pulled by the user with queries. Nodes can represent customers, companies, or any data a company chooses to record. Edges are formed by the database so that relationships between nodes are easily understood by the user. Businesses can utilize graph databases when they are pulling data and do not want to spend time organizing it into distinct relationships. Large enterprises may use complex queries to pull precise and in-depth information regarding their customer and user information or product tracking data, among other uses. Database administrators can scale high data values and still create usable models. Some businesses may choose to run an RDF database, a type of graph database that focuses on retrieving triples, or information organized in a subject-predicate-object relationship. Similar types of databases include [document database](https://www.g2.com/categories/document-databases) tools, [key-value store](https://www.g2.com/categories/key-value-stores) tools, [object-orientated database](https://www.g2.com/categories/object-oriented-databases) tools and more. Developers who are looking for an affordable solution can look to [free database software](https://learn.g2.com/free-database-software).

To qualify for inclusion in the Graph Database category, a product must:

- Provide data storage
- Record and represent data in a topographical schema
- Allow users to retrieve the data using query language





## Category Overview

**Total Products under this Category:** 68


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,000+ Authentic Reviews
- 68+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Best Graph Databases At A Glance

- **Leader:** [Arango](https://www.g2.com/products/arango/reviews)
- **Highest Performer:** [GraphJSON](https://www.g2.com/products/graphjson/reviews)
- **Easiest to Use:** [Amazon Neptune](https://www.g2.com/products/amazon-neptune/reviews)
- **Top Trending:** [Stardog](https://www.g2.com/products/stardog/reviews)
- **Best Free Software:** [Neo4j Graph Database](https://www.g2.com/products/neo4j-graph-database/reviews)


---

**Sponsored**

### Kintone

Kintone is a no-code business application platform designed to empower non-technical users to create robust applications, workflows, and databases tailored to their teams and organizations. By utilizing a user-friendly interface that emphasizes clicks over coding, Kintone enables individuals to develop applications that streamline business processes, enhance collaboration on projects and tasks, and facilitate the reporting of complex data with ease. This platform is particularly beneficial for business users who require immediate solutions without the need for extensive programming knowledge. Kintone offers a wide array of pre-built applications that cater to various use cases, including customer relationship management (CRM), project management, inventory management, and more. These templates allow users to hit the ground running and adapt the applications to their specific needs, significantly reducing the time and effort required to implement new systems. Kintone's target audience includes small to medium-sized businesses, project managers, team leaders, and any professional looking to optimize their workflow without relying on IT departments or external developers. The no-code approach democratizes app development, allowing users from diverse backgrounds to participate in creating solutions that address their unique challenges. This inclusivity fosters a culture of innovation within organizations, as team members can contribute ideas and improvements based on their firsthand experiences. Key features of Kintone include customizable dashboards, automated workflows, and real-time collaboration tools. Users can design dashboards that provide insights into their projects and data at a glance, while automated workflows help eliminate repetitive tasks, ensuring that team members can focus on higher-value activities. The platform also supports real-time collaboration, enabling teams to work together seamlessly, share updates, and track progress on projects without the need for constant meetings or email exchanges. Kintone stands out in the no-code platform category by offering a flexible and scalable solution that grows with organizations. Its ability to integrate with other tools and services further enhances its functionality, allowing users to create a comprehensive ecosystem that meets their evolving business needs. By providing a powerful yet accessible platform for app development, Kintone empowers users to take control of their workflows and drive efficiency within their teams.



[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=304&secure%5Bdisplayable_resource_id%5D=660&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=318&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=16406&secure%5Bresource_id%5D=304&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fgraph-databases&secure%5Btoken%5D=f9b9079959980699cea196a4f705ecff17166d325a804a2873b5fb2f09bda437&secure%5Burl%5D=https%3A%2F%2Fwww.kintone.com%2Fhow-to-get-started-with-kintone2%2F%3Futm_campaign%3DG2%2520Ads%26utm_source%3DG2%26utm_medium%3Dcpc%26utm_term%3DGet%2520started%2520CTA&secure%5Burl_type%5D=custom_url&secure%5Bvisitor_segment%5D=180)

---

## Top-Rated Products (Ranked by G2 Score)
### 1. [Arango](https://www.g2.com/products/arango/reviews)
  Arango provides a trusted data foundation for Contextual AI — transforming enterprise data into a System of Context that truly represents the business, so LLMs can deliver better outcomes with unlimited scale and cost efficiency. The Arango AI Data Platform gives developers a single, integrated environment to build and scale AI-powered applications without the complexity of stitching together multiple databases and tools. At its core is a massively scalable multi-model database that unifies graph, vector, document, and key-value data with full-text, geospatial, and vector search — creating the System of Context, the bridge between enterprise data and LLMs. The Arango AI Suite includes automated data pipelines, multimodal data ingestion, AIOps and MLOps, LLM integrations, Graph Analytics, agentic frameworks for context-aware Hybrid/GraphRAG, GraphML, natural-language support, and GPU acceleration — enabling repeatable ROI and faster innovation. Trusted by NVIDIA, HPE, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, Synopsys and Articul8, Arango powers enterprise AI with context, confidence, and scale. We are a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, YouTube, and G2.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 115

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.8/10)
- **Data Model:** 9.2/10 (Category avg: 8.8/10)
- **Data Types:** 8.9/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.5/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Arango](https://www.g2.com/sellers/arango)
- **Company Website:** https://arango.ai/
- **Year Founded:** 2015
- **HQ Location:** San Francisco, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/5289249/ (106 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Senior Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 57% Small-Business, 23% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (14 reviews)
- Features (10 reviews)
- Querying (7 reviews)
- Intuitive (6 reviews)
- Customization (5 reviews)

**Cons:**

- Poor Usability (5 reviews)
- Difficult Learning (4 reviews)
- Improvement Needed (4 reviews)
- Learning Curve (4 reviews)
- Learning Difficulty (4 reviews)

### 2. [Amazon Neptune](https://www.g2.com/products/amazon-neptune/reviews)
  Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Amazon Neptune is highly available, with read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones. Neptune is secure with support for encryption at rest. Neptune is fully-managed, so you no longer need to worry about database management tasks such as hardware provisioning, software patching, setup, configuration, or backups.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.8/10)
- **Data Model:** 9.4/10 (Category avg: 8.8/10)
- **Data Types:** 9.3/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 67% Small-Business, 23% Mid-Market


### 3. [Elastic Stack](https://www.g2.com/products/elastic-stack/reviews)
  The Elastic Stack, commonly known as the ELK Stack, is a comprehensive suite of open-source tools designed for ingesting, storing, analyzing, and visualizing data in real-time. It comprises Elasticsearch, Kibana, Beats, and Logstash, enabling users to handle data from any source and in any format efficiently. Key Features and Functionality: - Elasticsearch: A distributed, JSON-based search and analytics engine that allows for rapid storage, search, and analysis of large volumes of data. - Kibana: An extensible user interface that provides powerful visualizations, dashboards, and management tools to interpret and present data effectively. - Beats and Logstash: Data ingestion tools that collect and process data from various sources, transforming and forwarding it to Elasticsearch for indexing. - Integrations: A multitude of pre-built integrations that facilitate seamless data collection and connection with the Elastic Stack, enabling quick insights. Primary Value and User Solutions: The Elastic Stack empowers organizations to harness the full potential of their data by providing a scalable and resilient platform for real-time search and analytics. It addresses challenges such as managing large datasets, ensuring high availability, and delivering relevant search results swiftly. By offering a unified solution for data ingestion, storage, analysis, and visualization, the Elastic Stack enables users to gain actionable insights, enhance operational efficiency, and make informed decisions based on their data.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.8/10)
- **Data Model:** 10.0/10 (Category avg: 8.8/10)
- **Data Types:** 9.7/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Elastic](https://www.g2.com/sellers/elastic)
- **Year Founded:** 2012
- **HQ Location:** San Francisco, CA
- **Twitter:** @elastic (64,544 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/814025/ (4,986 employees on LinkedIn®)
- **Ownership:** NYSE: ESTC

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 45% Mid-Market, 34% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (3 reviews)
- Flexibility (3 reviews)
- Log Management (3 reviews)
- Search Efficiency (3 reviews)
- Versatility (3 reviews)

**Cons:**

- Resource Management (3 reviews)
- Complexity Issues (2 reviews)
- Expensive (2 reviews)
- High Memory Usage (2 reviews)
- Learning Curve (2 reviews)

### 4. [Neo4j Graph Database](https://www.g2.com/products/neo4j-graph-database/reviews)
  The fastest path to graph. Centered around the leading native graph database, today's Neo4j Graph Data Platform is a suite of applications and tools helping the world make sense of data. The Platform includes the Neo4j Graph Data Science Library – the leading enterprise-ready analytics workspace for graph data available as both open source and through a commercial license for enterprises – the graph visualization and exploration tool Bloom, the Cypher query language - very easy to learn and can operate across Neo4j, Apache Spark and Gremlin-based products using open source toolkits: "Cypher on Apache Spark (CApS) and Cypher for Gremlin.), Neo4j ETL and Kettle for data integration, and numerous additional tools, integrations and connectors to help developers and data scientists build graph-based solutions with ease. And the world's largest community to help enable any graph journey. Neo4j is the leading scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 131

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.8/10)
- **Data Model:** 7.7/10 (Category avg: 8.8/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Neo4j](https://www.g2.com/sellers/neo4j)
- **Year Founded:** 2007
- **HQ Location:** San Mateo, CA
- **Twitter:** @neo4j (46,969 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/neo4j/ (996 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 44% Small-Business, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (2 reviews)
- Features (2 reviews)
- Database Management (1 reviews)
- Design Flexibility (1 reviews)
- Flexibility (1 reviews)

**Cons:**

- Learning Curve (2 reviews)
- Backup Issues (1 reviews)
- Data Management Issues (1 reviews)
- Difficult Learning (1 reviews)
- Import Issues (1 reviews)

### 5. [GraphJSON](https://www.g2.com/products/graphjson/reviews)
  Serverless, self-serve and affordable analytics designed to help you get the most out of your data.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 34

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.8/10)
- **Data Model:** 8.7/10 (Category avg: 8.8/10)
- **Data Types:** 7.9/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [GraphJSON](https://www.g2.com/sellers/graphjson)
- **HQ Location:** N/A
- **Twitter:** @GraphJSON (516 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 53% Small-Business, 36% Mid-Market


### 6. [OrientDB](https://www.g2.com/products/orientdb/reviews)
  OrientDB is the first Multi-Model Distributed DBMS with a True Graph Engine. Multi-Model means 2nd generation NoSQL able to manage complex domain with incredible performance. OrientDB manages relationships without using JOINs, but rather direct pointers. This allows to have constant performance on traversing relationships, no matter the database size.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 58

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 8.8/10)
- **Data Model:** 8.6/10 (Category avg: 8.8/10)
- **Data Types:** 7.8/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,246 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)
- **Ownership:** NYSE:SAP

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 47% Small-Business, 42% Mid-Market


### 7. [Stardog](https://www.g2.com/products/stardog/reviews)
  Stardog is a reusable, scalable knowledge graph platform that enables enterprises to unify all their data, including data sources and databases of every type, to get the answers needed to drive business decisions. Stardog is an enterprise knowledge graph platform that allows customers to query massive, disparate, heterogeneous data regardless of structure with simplicity of implementation. Stardog’s enterprise customers include Fortune 500 companies in finance, healthcare, life sciences, energy, media, and government.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 17

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.8/10)
- **Data Model:** 9.3/10 (Category avg: 8.8/10)
- **Data Types:** 8.1/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Stardog Union](https://www.g2.com/sellers/stardog-union)
- **HQ Location:** Arlington, VA
- **Twitter:** @StardogHQ (3,970 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10917244 (95 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 39% Small-Business, 33% Enterprise


### 8. [FlockDB](https://www.g2.com/products/flockdb/reviews)
  FlockDB is simpler than other graph databases because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites.


  **Average Rating:** 3.6/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Twitter](https://www.g2.com/sellers/twitter)
- **Year Founded:** 2006
- **HQ Location:** San Francisco, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/96622/ (1,004 employees on LinkedIn®)
- **Ownership:** NYSE: TWTR
- **Total Revenue (USD mm):** $3,716

**Reviewer Demographics:**
  - **Company Size:** 36% Mid-Market, 36% Small-Business


### 9. [Tigergraph](https://www.g2.com/products/tigergraph/reviews)
  TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10)
- **Data Model:** 9.3/10 (Category avg: 8.8/10)
- **Data Types:** 8.5/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Tigergraph](https://www.g2.com/sellers/tigergraph)
- **Year Founded:** 2012
- **HQ Location:** Redwood City, CA
- **Twitter:** @TigerGraphDB (12,677 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3693966 (139 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 55% Enterprise, 36% Mid-Market


### 10. [Dgraph](https://www.g2.com/products/dgraph/reviews)
  Dgraph is the world's most advanced GraphQL database with a graph backend. The number one graph database on GitHub and over 500,000 downloads every month, Dgraph is built for performance and scalability. Jepsen tested, it has the best performance, returning millisecond query responses on terabytes of data. Dgraph is ideal for a range of use cases, from customer 360 and fraud detection to complicated queries with multi-hops and arbitrary-depth joins. Strong performance and memory management make the graph database ideal for enterprises while Dgraph Cloud makes it quick and easy for app developers to launch a project over the weekend. Scale from zero to billions of records effortlessly. Available in open source and hosted versions (Dgraph Cloud) and enterprise license.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.8/10 (Category avg: 8.8/10)
- **Data Model:** 9.7/10 (Category avg: 8.8/10)
- **Data Types:** 9.5/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.4/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Dgraph Labs](https://www.g2.com/sellers/dgraph-labs)
- **Year Founded:** 2016
- **HQ Location:** San Francisco, CA
- **Twitter:** @dgraphlabs (16 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/13183384/ (19 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 68% Small-Business, 18% Enterprise


### 11. [GraphQL](https://www.g2.com/products/graphql/reviews)
  GraphQL is an open-source data query language and runtime designed to streamline API development by enabling clients to request precisely the data they need. Developed internally by Facebook in 2012 and publicly released in 2015, GraphQL has become a foundational tool for modern application development, offering a more efficient and flexible alternative to traditional REST APIs. Key Features and Functionality: - Hierarchical Structure: GraphQL queries mirror the shape of the response data, making it intuitive for developers to predict and structure their requests. - Strong Typing: Each element in a GraphQL schema is explicitly typed, allowing for clear definitions of data structures and enabling robust validation and tooling support. - Introspection: GraphQL APIs are self-describing, allowing clients to query the schema for available types and operations, which facilitates dynamic client development and enhances discoverability. - Protocol Agnostic: GraphQL operates independently of any specific storage or transport protocol, enabling seamless integration with various databases and existing infrastructure. - Version-Free Evolution: The flexibility of GraphQL allows for the addition of new fields and types without impacting existing queries, eliminating the need for versioning and simplifying API evolution. Primary Value and Problem Solving: GraphQL addresses several challenges inherent in traditional API development: - Optimized Data Retrieval: By allowing clients to specify exact data requirements, GraphQL minimizes over-fetching and under-fetching of data, leading to more efficient network usage and improved application performance. - Enhanced Developer Productivity: The self-documenting nature of GraphQL schemas, combined with strong typing and introspection capabilities, accelerates development cycles and reduces the likelihood of errors. - Flexibility Across Platforms: GraphQL's language-agnostic design and support for multiple programming languages enable consistent API consumption across diverse platforms, including web, mobile, and IoT devices. - Simplified API Maintenance: The ability to evolve APIs without versioning complexities allows for smoother updates and feature additions, ensuring long-term maintainability and scalability. By providing a more efficient, flexible, and developer-friendly approach to API design, GraphQL empowers organizations to build high-performance applications that can adapt to evolving requirements and deliver superior user experiences.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [The GraphQL Foundation](https://www.g2.com/sellers/the-graphql-foundation)
- **Year Founded:** 2019
- **HQ Location:** San Francisco, US
- **Twitter:** @GraphQL (126,378 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/51722505 (10 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 64% Mid-Market, 36% Small-Business


### 12. [GraphBase](https://www.g2.com/products/graphbase/reviews)
  GraphBase is a second generation Graph Database Management System (DBMS). Built for 21st Century data problems, GraphBase is a game-changer when it comes to handling large, complex data structures.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.8/10)
- **Data Model:** 7.8/10 (Category avg: 8.8/10)
- **Data Types:** 8.1/10 (Category avg: 8.8/10)
- **Built - In Search:** 6.9/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [FactNexus](https://www.g2.com/sellers/factnexus)
- **Year Founded:** 2010
- **HQ Location:** Sydney
- **Twitter:** @AskKayBot (5 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1546147 (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 38% Mid-Market


### 13. [Cayley](https://www.g2.com/products/cayley/reviews)
  Cayley is an open-source graph written in Go inspired by the graph database behind Freebase and Google's Knowledge Graph.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 6.7/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Cayley](https://www.g2.com/sellers/cayley)
- **HQ Location:** N/A
- **Twitter:** @cayleygraph (731 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Small-Business, 36% Mid-Market


### 14. [Redis Cloud](https://www.g2.com/products/redis-cloud/reviews)
  Redis Cloud is our fully-managed Redis Enterprise service, delivering unmatched speed, simplicity, and scalability. It's perfect for cloud-native applications requiring real-time data processing, without the hassle of managing infrastructure. Redis Cloud surpasses Redis-compatible cloud services built on open source such as Amazon ElastiCache and Google Cloud Memorystore by offering enterprise-grade features like active-active geo-distribution, advanced query and search capabilities, seamless data synchronization, and multi-cloud support.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 42

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Redis](https://www.g2.com/sellers/redis)
- **Year Founded:** 2011
- **HQ Location:** San Francisco, CA
- **Twitter:** @Redisinc (43,961 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2014725/ (1,510 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 50% Small-Business, 41% Mid-Market


### 15. [EdgeDB](https://www.g2.com/products/edgedb/reviews)
  Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects with properties connected by links.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 8

**User Satisfaction Scores:**

- **Data Model:** 7.0/10 (Category avg: 8.8/10)
- **Data Types:** 7.1/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [EdgeDB](https://www.g2.com/sellers/edgedb)
- **Year Founded:** 2019
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/edgedb/ (24 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 22% Mid-Market


### 16. [IBM Graph](https://www.g2.com/products/ibm-graph/reviews)
  IBM Graph is a fully managed property graph-as-a-service that enables you to store, query and visualize data points, connections and properties. Highly available Provides service that is always up and ensures your data is always accessible, so your web and mobile apps are constantly working for your business. Managed 24/7 Our experts monitor, manage and optimize everything in your stack, every day, all day. Enables your development team to focus on building apps, instead of worrying about the graph. Scales seamlessly Lets you start small and scale on demand as your data size and complexity increases, enabling your application to grow with your business.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 8

**User Satisfaction Scores:**

- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 7.2/10 (Category avg: 8.8/10)
- **Built - In Search:** 7.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,390 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Company Size:** 50% Enterprise, 30% Small-Business


### 17. [Oracle Spatial and Graph](https://www.g2.com/products/oracle-spatial-and-graph/reviews)
  Oracle Spatial and Graph supports a full range of geospatial data and analytics for land management and GIS, mobile location services, sales territory management, transportation, LiDAR analysis and location-enabled Business Intelligence. The graph features include RDF graphs for applications ranging from semantic data integration to social network analysis to linked open data and network graphs used in transportation, utilities, energy and telcos and drive-time analysis for sales and marketing applications.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 8

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.5/10 (Category avg: 8.8/10)
- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 9.0/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Oracle](https://www.g2.com/sellers/oracle)
- **Year Founded:** 1977
- **HQ Location:** Austin, TX
- **Twitter:** @Oracle (827,868 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (199,301 employees on LinkedIn®)
- **Ownership:** NYSE:ORCL

**Reviewer Demographics:**
  - **Company Size:** 44% Mid-Market, 44% Small-Business


### 18. [Azure Cosmos DB](https://www.g2.com/products/azure-cosmos-db/reviews)
  Azure Cosmos DB is a fully managed, globally distributed NoSQL and vector database service designed to support mission-critical applications with ultra-low latency and elastic scalability. It enables developers to build AI-powered applications and agents by providing seamless integration with AI services, allowing for efficient storage and querying of both NoSQL data and vectors. With its schema-agnostic JSON document model, Azure Cosmos DB simplifies the development process by automatically indexing all data, eliminating the need for manual schema or index management. The service offers comprehensive Service Level Agreements (SLAs), ensuring less than 10-millisecond read and write latencies and 99.999% availability, making it a reliable choice for applications requiring high performance and global reach. Key Features and Functionality: - Global Distribution: Azure Cosmos DB allows for turnkey global distribution, enabling data to be replicated across multiple regions worldwide, providing high availability and low latency access to data. - Elastic Scalability: The service offers elastic scaling of throughput and storage, allowing developers to scale resources up or down based on demand without downtime. - Multi-Model Support: It natively supports multiple data models, including document, key-value, graph, and column-family, catering to diverse application needs. - AI Integration: Built-in vector search capabilities simplify the development of AI applications by efficiently storing and querying vectors alongside NoSQL data. - Automatic Indexing: All data is automatically indexed, facilitating fast and efficient queries without the need for manual index management. - Comprehensive SLAs: Azure Cosmos DB provides industry-leading SLAs covering throughput, latency, availability, and consistency, ensuring predictable performance. Primary Value and Solutions Provided: Azure Cosmos DB addresses the challenges of building and managing globally distributed applications by offering a fully managed database service that ensures high availability, low latency, and elastic scalability. Its integration with AI services and support for multiple data models empower developers to create intelligent, responsive applications without the complexity of managing infrastructure. By automatically handling data distribution, scaling, and indexing, Azure Cosmos DB allows organizations to focus on innovation and delivering value to their users, making it an ideal solution for applications requiring real-time data access and global reach.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 59

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10)
- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,114,353 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Enterprise, 28% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (4 reviews)
- Features (3 reviews)
- Integrations (3 reviews)
- Scalability (3 reviews)
- Customization (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Cost Issues (2 reviews)
- Complexity Issues (1 reviews)
- Complex Usage (1 reviews)
- Cost Increase (1 reviews)

### 19. [Fauna](https://www.g2.com/products/fauna-fauna/reviews)
  Fauna is a truly serverless operational database that empowers teams to ship applications faster. It combines the flexibility of a document model with the strong consistency and rich querying power of relational systems—all built on a serverless, distributed architecture that scales automatically, without the complexity of manual provisioning, sharding, or replication. Over 80,000 development teams choose Fauna to build and scale modern transactional applications including teams from Tyson Foods, Unilever, Lexmark, Intelliculture, Hannon Hill, Cloaked, DTLR, and Insights.gg.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 25

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.8/10)
- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 9.2/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Fauna](https://www.g2.com/sellers/fauna)
- **Year Founded:** 2015
- **HQ Location:** San Francisco, CA
- **Twitter:** @fauna (93,324 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/faunadb/ (11 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 64% Small-Business, 24% Mid-Market


#### Pros & Cons

**Pros:**

- Scalability (6 reviews)
- Ease of Use (5 reviews)
- Flexibility (4 reviews)
- Customer Support (3 reviews)
- Easy Integrations (3 reviews)

**Cons:**

- Difficult Learning (3 reviews)
- Poor Documentation (2 reviews)
- Complexity (1 reviews)
- Complex Setup (1 reviews)
- Cost Issues (1 reviews)

### 20. [RDFox](https://www.g2.com/products/rdfox/reviews)
  RDFox is a high-performance in-memory knowledge graph and semantic reasoner. Optimised for speed and advanced reasoning, it affords query and loading times that are orders of magnitudes faster than alternative triplestores, while also achieving greater insights into the data. RDFox is developed by Oxford Semantic Technologies—an Oxford University spin-out founded by leading academics backed by decades of cutting-edge research in semantic web technologies.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10)
- **Data Model:** 10.0/10 (Category avg: 8.8/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Built - In Search:** 7.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Oxford Semantic Technologies](https://www.g2.com/sellers/oxford-semantic-technologies)
- **Year Founded:** 2017
- **HQ Location:** Oxford, GB
- **Twitter:** @oxfordsemantic (1,749 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/oxford-semantic-technologies/ (21 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 36% Small-Business, 29% Enterprise


### 21. [Redis Software](https://www.g2.com/products/redis-software/reviews)
  Redis Software is our advanced solution delivering unmatched speed and reliability for on-prem and private cloud environments. It gives you full control over your deployment, ensuring high performance and scalability to meet your specific needs. Redis Software builds on the speed and reliability of Redis Community Edition with advanced features like active-active geo-distribution, advanced query and search capabilities, automated data synchronization, and superior security features. These enhancements provide enterprise-grade performance, reliability, and security, making Redis Software the ideal choice for production-grade applications.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 130

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.8/10)
- **Data Model:** 7.5/10 (Category avg: 8.8/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Built - In Search:** 7.5/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Redis](https://www.g2.com/sellers/redis)
- **Year Founded:** 2011
- **HQ Location:** San Francisco, CA
- **Twitter:** @Redisinc (43,961 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2014725/ (1,510 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Small-Business, 26% Mid-Market


#### Pros & Cons

**Pros:**

- Useful (2 reviews)
- Cost Efficiency (1 reviews)
- Customization (1 reviews)
- Data Storage (1 reviews)
- Ease of Setup (1 reviews)

**Cons:**

- Data Size Limitations (1 reviews)
- Expensive (1 reviews)
- Limited Chart Features (1 reviews)
- Poor UI (1 reviews)
- Slow Performance (1 reviews)

### 22. [Ultipa Graph](https://www.g2.com/products/ultipa-graph/reviews)
  Ultipa builds category-defining real-time graph XAI & database products, and empowers smart enterprises with graph augmented intelligence. Enterprises around the world are going through a major digital transformation trend, which asks for data intelligence + infrastructure revolution. Traditional SQL/RDMBS and most NoSQLs/ML/AI frameworks are outdated, sluggish, black-box and un-flexible. Ultipa graph database products aim to solve these problems w/ real-time graph computing, flexible data modeling, eXplainable AI (XAI) and graph-augmented intelligence. We started in 2019 and began commercialization in 2021, since then we have been serving some of the world's largest banks, insurance companies, regulators and enterprises, and we have applied Ultipa graph database into vertical industries that no other graph vendors could have imagined, such as liquidity risk management, previously only mega players like Oracle had solution which takes T+1 while Ultipa takes


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 5

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10)
- **Data Model:** 9.6/10 (Category avg: 8.8/10)
- **Data Types:** 9.6/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.6/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Ultipa](https://www.g2.com/sellers/ultipa)
- **Year Founded:** 2019
- **HQ Location:** Pleasanton, US
- **LinkedIn® Page:** https://www.linkedin.com/company/ultipa (60 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 40% Mid-Market, 40% Small-Business


### 23. [data.world](https://www.g2.com/products/data-world/reviews)
  data.world is the most-adopted data catalog and governance platform on the market. Built on a unique knowledge graph foundation, data.world seamlessly integrates with your existing systems. We set the standard for swift, people-centric governance. We don't just manage data; we unlock its potential, paving the way for responsible AI adoption and data-driven decision-making at scale. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10)
- **Data Model:** 8.9/10 (Category avg: 8.8/10)
- **Data Types:** 9.4/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.9/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [data.world](https://www.g2.com/sellers/data-world)
- **Year Founded:** 2016
- **HQ Location:** Austin, Texas
- **Twitter:** @datadotworld (5,515 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/data.world/ (107 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 25% Mid-Market


#### Pros & Cons

**Pros:**

- Analytics (1 reviews)
- Data Discovery (1 reviews)
- Data Management (1 reviews)
- Data Visualization (1 reviews)
- Ease of Use (1 reviews)

**Cons:**

- Poor Customer Support (1 reviews)
- Poor Support Services (1 reviews)

### 24. [FalkorDB](https://www.g2.com/products/falkordb/reviews)
  An ultra-low latency Graph Database that perfects the Knowledge Graph for GraphRAG. Effectively overcoming the existing limitations of RAG for GenAI & Large Language Models (LLM).


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 4

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.5/10 (Category avg: 8.8/10)
- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 6.7/10 (Category avg: 8.8/10)
- **Built - In Search:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [FalkorDB](https://www.g2.com/sellers/falkordb)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


### 25. [HugeGraph](https://www.g2.com/products/hugegraph/reviews)
  HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin(a powerful graph traversal language).


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 4

**User Satisfaction Scores:**

- **Data Model:** 8.3/10 (Category avg: 8.8/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Built - In Search:** 9.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [HugeGraph](https://www.g2.com/sellers/hugegraph)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 75% Mid-Market, 50% Small-Business




## Parent Category

[IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure)



## Related Categories

- [Document Databases](https://www.g2.com/categories/document-databases)
- [Key Value Databases](https://www.g2.com/categories/key-value-databases)
- [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas)



---

## Buyer Guide

### What You Should Know About Graph Databases

### What are Graph Databases?

Graph databases are designed for depicting relationships (_edges_) between data points (_nodes_). Less structurally rigid than relational databases, graph databases allow nodes to have a multitude of edges; that is, there’s no limit on the number of relationships a node can have. (An example of this is in the following section.) Additionally, each edge can have multiple characteristics which define it. There is no formal limit—nor standardization—on how many edges each node can have, nor how many characteristics an edge can have. Graph databases can also contain many different pieces of information that would not necessarily be normally related.

Each node is defined by pieces of information called _properties_. Properties could be names, dates, identification numbers, basic descriptors, or other information—anything that would describe the node itself. Nodes are connected by edges, which can be _directed_ or _undirected_. Like in mathematical graph theory, an undirected edge is _bidirectional_; that is, a relationship can be carried from node A to node B, and from node B to node A. A directed edge, however, only carries meaning in one direction, say from node B to node A.

Key Benefits of Graph Databases

- Organize a variety of data without rigid structures
- Offer flexible scaling and adjustment inherently
- Describe numerous data relationship characteristics simultaneously

### Why Use Graph Databases?

Graph databases are ideal for storing and retrieving information that is independent but related in multiple ways. For example, say a user wanted to map a group of friends. Each friend would be a node, with edges between each friend with a characteristic “friends." But, say two of those friends are coworkers; then, their edge would also have a characteristic “coworkers." Edges can get further definition by adding common interests, personal experiences, and so on.

Because graph databases are, by design, most conducive to organizing broad sets of data through which there are not uniform relationships or kinds of data, they can be invaluable tools for social mapping, master data management, knowledge graphing/ontology, infrastructure mapping, recommendation engines, and more. A business could set each node to be one of their products, and let edges draw recommendation relationships based on what product a consumer might buy. It could also map relationships between contacts, departments, and more.

Graph databases are flexible and scalable by design, so a business user would not need to know an exact or complete use case for a graph database before creating it. Expanding a graph database is a matter of adding new nodes and any potential edges which might be associated with them.

### Who Uses Graph Databases?

Like other databases, graph databases are primarily maintained by a database administrator or team. That said, because of their wide range of coverage, graph databases are often accessed by several organizations within a company. Development, IT, billing, and more would all have valid reasons for needing access to graph databases, pending their assigned uses within the company.

### Graph Databases Features

Graph database solutions will typically have the following features.

Database creation and maintenance — Graph databases allow users to easily build and maintain a database(s).

CRUD operations — An acronym for _create, read, update, and delete_, CRUD operations delineate basic operations of many databases. Graph databases should be able to perform these operations and usually can with similar capability to the most notable CRUD-oriented database type, relational.

Scalability and flexibility — Graph databases can grow and expand with business requirements. Unlike some other database solutions, they can scale more quickly with less worry about strict data organization, relying instead on developing relationships between new and existing nodes.

Simplified querying — Graph databases can skip some larger query complexities, bypassing things like foreign keys, nested queries, and join statements in favor of direct or transitive relationships.

OS compatibility — Graph databases do not require any one specific operating system to run, making them a flexible choice for any operating system.

### Trends Related to Graph Databases

Graph vs. relational — The graph database vs. relational database discussion is an ongoing point of conflict for database users and administrators alike. Graph databases generally lend themselves to more fluid data querying off simpler querying syntax, and are generally better at scaling without needing to prepare new or specific schema. But, relational databases’ schema rigidity and data normalization can be extremely beneficial in some use cases, and they are also generally more conducive to security and privacy policy implementation and enforcement.

### Potential Issues with Graph Databases

Security and privacy — As alluded to above, graph databases can struggle with security and privacy situations. They require more strict implementations of security and access measures. Since graph databases are more oriented toward mapping relationships, that structure can also be utilized in ways that could raise privacy concerns, such as revealing a more laid-bare view of a client or customer—and every other potential client or customer to which they are related. Businesses implementing graph databases should take extra care to secure both how these databases are accessed, and the databases themselves.

Data integrity implications — Graph databases simplify the ways in which information relates to other information. In doing so, by shortening or condensing the relationship (as compared to, say, traversing numerous tables in a relational database), it’s particularly vital that all data in a graph database is accurate. One improperly aligned relationship can directly lead to incorrect data, unlike in a relational database where improper data might hit a snag during a nested query, throw an error, and out the issue. So, in using graph databases, data integrity is of particularly high importance.

### Software and Services Related to Graph Databases

Many conversations around graph databases are contextualized by one or both of the following alternatives.

[RDF databases](https://www.g2.com/categories/rdf-databases) — A type of graph database, resource description framework (RDF) or _triplestore_ databases function around the concept of storing data as triples. Triples—in a “subject–predicate–object" format—are used specifically to describe the relationship between two things.

[Relational databases](https://www.g2.com/categories/relational-databases) — Relational databases—the standard “rows and columns" data stores—had been the standard for databases virtually since inception. They carry with them significantly more rigid structure than graph databases, which can be extremely beneficial for tracking large volumes of like data but might make it more complicated to follow relationships between that data.