# Best Graph Database Solutions - Page 3 *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 ## How Many Graph Databases Products Does G2 Track? **Total Products under this Category:** 68 ### Category Stats (May 2026) - **Average Rating**: 4.48/5 - **New Reviews This Quarter**: 1 - **Buyer Segments**: Enterprise 67% │ Small-Business 33% - **Top Trending Product**: Elastic Stack (+0.018) *Last updated: May 18, 2026* ## How Does G2 Rank Graph Databases Products? **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. ## Which Graph Databases Is Best for Your Use Case? - **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=e38cc080ab53bf220f1ea718565c63bfc0068870a1d0c3790457ee7a38d45f5e&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) --- ## What Are the Top-Rated Graph Databases Products in 2026? ### 1. [Aerospike Graph](https://www.g2.com/products/aerospike-graph/reviews) Aerospike Graph is a high-performance, distributed graph database designed to manage and query extensive graph datasets with exceptional speed and scalability. Built upon the robust Aerospike Database, it leverages the Apache TinkerPop framework to support complex graph computations. Aerospike Graph is ideal for applications such as customer 360, identity graphs, and fraud detection, offering real-time performance even at enterprise-scale data volumes. **Who Is the Company Behind Aerospike Graph?** - **Seller:** [Aerospike](https://www.g2.com/sellers/aerospike) - **Year Founded:** 2009 - **HQ Location:** Mountain View, CA - **Twitter:** @aerospikedb (7,833 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2696852/ (306 employees on LinkedIn®) ### 2. [BangDB](https://www.g2.com/products/iqlect-bangdb/reviews) BangDB is a platform that provides an end-to-end solution for real-time big data analytics process. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind BangDB?** - **Seller:** [IQLECT](https://www.g2.com/sellers/iqlect) - **HQ Location:** Bangalore, India - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are BangDB's Pros and Cons? **Pros:** - Options (1 reviews) - Search Efficiency (1 reviews) - Visibility (1 reviews) **Cons:** - Difficult Learning (1 reviews) - Learning Difficulty (1 reviews) ### 3. [Data Graphs](https://www.g2.com/products/data-graphs/reviews) The fastest and most usable knowledge graph database. Launch intelligent products in record time. Data Graphs is the fastest enterprise Knowledge Graph Database platform, both in performance and in speed of setup and launch. Model, load, explore, integrate and roll out with ease. No need to worry about tech complexity. Robust data governance is built right in. **Who Is the Company Behind Data Graphs?** - **Seller:** [Data Language](https://www.g2.com/sellers/data-language) - **Year Founded:** 2014 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/datalanguage (14 employees on LinkedIn®) ### 4. [Enterprise GraphQL Platform](https://www.g2.com/products/enterprise-graphql-platform/reviews) Grafbase is a GraphQL platform designed for managing federated graphs in enterprise environments, across distributed systems. It provides a unified gateway that allows teams to compose APIs from multiple data sources using a declarative CLI, configuration model, and extensible runtime. Grafbase includes a schema registry, schema checks, support for persisted queries, and tooling for local development and deployment automation. Extensions enable secure integration with third-party systems and allow incremental adoption of federation across teams and environments. Security capabilities include authentication, authorization, access tokens, message signing, and trusted documents. Traffic controls such as rate limiting, operation limits, and query complexity rules are built in. Grafbase supports both self-hosted and managed cloud deployments, with advanced configuration options designed to support platform engineering practices, infrastructure controls, and SDLC governance models. **Who Is the Company Behind Enterprise GraphQL Platform?** - **Seller:** [Grafbase](https://www.g2.com/sellers/grafbase) - **Year Founded:** 2021 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://linkedin.com/company/grafbase (13 employees on LinkedIn®) ### 5. [Fluree](https://www.g2.com/products/fluree/reviews) Fluree is a comprehensive data management platform designed to create secure, interoperable, and verifiable data ecosystems. By integrating blockchain technology with a graph database structure, Fluree ensures data integrity, facilitates seamless data sharing, and supports complex data relationships. This approach empowers organizations to build trusted, linked, and composable data systems that can scale across applications, organizations, and devices. Key Features and Functionality: - Fluree Core: An open-source knowledge graph database that embeds digital trust, data-driven access policies, and linked data standards. It supports multiple query languages, including SPARQL, GraphQL, SQL, and FlureeQL, and offers fine-grained access control. - Fluree Sense: An AI-driven pipeline that ingests, classifies, and masters structured data, transforming raw business data into semantic golden records. It automates data discovery, cleansing, and mapping processes, enhancing data quality and interoperability. - Fluree CAM : Utilizes natural language processing to automatically classify and tag unstructured digital assets, converting them into organized, semantically rich content. - Fluree ITM : Enables the creation and maintenance of controlled vocabularies, from simple taxonomies to complex ontologies, facilitating consistent data modeling and management. Primary Value and User Solutions: Fluree addresses the challenges of data silos, security, and interoperability by providing a unified platform that embeds trust and governance directly into the data layer. Organizations can leverage Fluree to: - Ensure Data Integrity and Security: By combining blockchain technology with a graph database, Fluree offers tamper-proof data storage and fine-grained access control, ensuring data remains secure and verifiable. - Facilitate Seamless Data Sharing: Fluree's support for linked data standards and its composable architecture enable organizations to break down data silos, allowing for efficient data sharing across applications, departments, and external partners. - Enhance Data Quality and Interoperability: Through AI-driven tools like Fluree Sense and Fluree CAM, organizations can automate the cleansing, classification, and mapping of both structured and unstructured data, resulting in high-quality, interoperable data assets. - Support Advanced Analytics and AI Initiatives: Fluree's knowledge graph database structure and semantic capabilities provide a robust foundation for advanced analytics, machine learning, and AI applications, enabling organizations to derive deeper insights and drive innovation. By integrating these features, Fluree empowers organizations to build intelligent data ecosystems that are secure, scalable, and ready to meet the demands of modern data management and analytics. **Who Is the Company Behind Fluree?** - **Seller:** [Fluree](https://www.g2.com/sellers/fluree) - **Year Founded:** 2017 - **HQ Location:** Winston Salem, US - **LinkedIn® Page:** https://www.linkedin.com/company/fluree-pbc/ (50 employees on LinkedIn®) ### 6. [Gemini Data](https://www.g2.com/products/gemini-data/reviews) Gemini Enterprise is a cloud-native, no-code platform designed to seamlessly integrate generative AI with your organization's critical business data. By creating a contextual layer atop existing data sources, it enables rapid decision-making through a secure and trustworthy AI experience. This user-friendly platform allows both technical and non-technical users to connect, manage, and analyze data without the need for specialized IT skills, accelerating the adoption of AI-driven insights across the enterprise. Key Features and Functionality: - Data Connectivity: Easily connect to various enterprise data sources, including sales, finance, products, supply chain, and security, facilitating comprehensive data integration. - No-Code Interface: Utilize a wizard-driven, no-code environment that simplifies data importation and modeling, eliminating the need for complex coding or query languages. - Semantic Layer Creation: Develop a semantic understanding of your data to enhance accuracy, specificity, and explainability, thereby minimizing AI hallucinations. - Generative AI Agnostic: Apply the latest generative AI models to your business data without requiring machine learning expertise, fine-tuning, or additional training. - Enterprise AI Assistant: Interact with an AI assistant capable of answering business-related questions and generating analytical reports, streamlining information retrieval and reporting processes. Primary Value and Problem Solved: Gemini Enterprise addresses the challenge of integrating generative AI into existing business infrastructures by providing a straightforward, secure, and efficient platform. It empowers organizations to harness AI capabilities without the typical complexities associated with data integration and model training. By enabling rapid deployment and broad adoption, Gemini Enterprise enhances decision-making processes, improves operational efficiency, and delivers a higher return on AI investments. **Average Rating:** 3.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Gemini Data?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Gemini Data?** - **Seller:** [Gemini Data](https://www.g2.com/sellers/gemini-data) - **Year Founded:** 2015 - **HQ Location:** Greenbrae, US - **LinkedIn® Page:** https://www.linkedin.com/company/gemini-data-inc (29 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business ### 7. [Graph Story](https://www.g2.com/products/graph-story/reviews) Graph Story provides graph databases, applications and solutions as a service. **Who Is the Company Behind Graph Story?** - **Seller:** [Graph Story](https://www.g2.com/sellers/graph-story) - **Year Founded:** 2014 - **HQ Location:** Memphis, US - **Twitter:** @graphstoryco (582 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/graph-story/ (6 employees on LinkedIn®) ### 8. [IBM Compose for JanusGraph](https://www.g2.com/products/ibm-compose-for-janusgraph/reviews) JanusGraph is a scalable graph database optimized for storing and querying highly-interconnected data and provides you with simple and efficient data retrieval from complex structures **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate IBM Compose for JanusGraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind IBM Compose for JanusGraph?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,796 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 9. [Macrometa](https://www.g2.com/products/macrometa/reviews) Macrometa is a hyper-distributed cloud platform featuring a Global Data Network (GDN) and PhotonIQ, an AI-powered Edge Delivery Network. With over 175 points of presence (PoPs) worldwide, Macrometa empowers enterprises to build real-time apps and APIs that store, process, and serve data within milliseconds to users globally. PhotonIQ: AI-Driven Edge Services PhotonIQ, Macrometa's suite of edge services, leverages AI and machine learning to deliver faster, more efficient, and secure digital experiences across various sectors including eCommerce, Gaming, and Financial Services. Key PhotonIQ services include: Performance Proxy (P3): Improves Core Web Vitals and optimizes web assets Dynamic Prerendering: Enhances site speed and SEO Virtual Waiting Rooms: Manages high-traffic scenarios Digital Fingerprinting: Enables privacy-preserving user tracking Unparalleled Performance Macrometa's platform ensures global P90 roundtrip response times of under 50ms, with the ability to serve results in under 50ms globally. This ultra-fast performance can significantly boost conversions, with increases of up to 72% reported for app performance improvements. Cost-Efficient and Developer-Friendly Macrometa's efficient architecture typically reduces cloud spend by 50% or more compared to traditional providers. The platform accelerates development cycles, allowing enterprises to implement PhotonIQ's AI at the edge services in 60 days or less. Developers can leverage Macrometa's API in any programming language, facilitating rapid product development and feature deployment without requiring expertise in distributed systems. By combining cutting-edge technology with ease of use, Macrometa enables businesses to deliver exceptional user experiences, drive organic traffic, and achieve substantial improvements in web performance and security. **Who Is the Company Behind Macrometa?** - **Seller:** [Macrometa](https://www.g2.com/sellers/macrometa) - **Year Founded:** 2017 - **HQ Location:** Palo Alto, US - **Twitter:** @macrometa (397 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/macrometa-corporation (54 employees on LinkedIn®) ### 10. [mapgraph](https://www.g2.com/products/mapgraph/reviews) mapgraph has a basic in-memory database for storing linked maps in Clojure and ClojureScript **Who Is the Company Behind mapgraph?** - **Seller:** [mapgraph](https://www.g2.com/sellers/mapgraph) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 11. [OrigoDB](https://www.g2.com/products/origodb/reviews) OrigoDB enables you to build high quality, mission critical systems with real-time performance at a fraction of the time and cost. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind OrigoDB?** - **Seller:** [OrigoDB](https://www.g2.com/sellers/origodb) - **Year Founded:** 1989 - **HQ Location:** EDINBURGH, GB - **LinkedIn® Page:** https://www.linkedin.com/company/origo-services-ltd/?originalSubdomain=uk (160 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 12. [Relay Framework](https://www.g2.com/products/relay-framework/reviews) Relay is designed for high performance at any scale. Relay keeps management of data-fetching easy, whether your app has tens, hundreds, or thousands of components. And thanks to Relay’s incremental compiler, it keeps your iteration speed fast even as your app grows. **Who Is the Company Behind Relay Framework?** - **Seller:** [Relay.dev](https://www.g2.com/sellers/relay-dev) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 13. [SAP Knowledge Graph](https://www.g2.com/products/sap-knowledge-graph/reviews) Drive high-performance business processes with AI that understands the full context of your data. **Who Is the Company Behind SAP Knowledge Graph?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,206 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®) - **Ownership:** NYSE:SAP ### 14. [Sparksee](https://www.g2.com/products/sparksee/reviews) Sparksee (formerly known as DEX) is a graph database that makes space and performance compatible with a small footprint and a fast analysis of large networks. **Who Is the Company Behind Sparksee?** - **Seller:** [Sparsity Technologies](https://www.g2.com/sellers/sparsity-technologies) - **Year Founded:** 2010 - **HQ Location:** Barcelona, ES - **Twitter:** @SparsityTech (874 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sparsity-technologies/ (14 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 15. [ThingSpan enterprise graph and data fusion platform](https://www.g2.com/products/thingspan-enterprise-graph-and-data-fusion-platform/reviews) ThingSpan is an enterprise-grade, massively scalable distributed platform designed for graph analytics and real-time relationship discovery. It enables organizations to analyze connections across multiple data sources in real-time, facilitating rapid navigation and pattern-finding within complex datasets. By integrating with industry-standard open-source technologies such as Apache Spark, Kafka, YARN, and Hadoop, ThingSpan ensures cost-effective scalability on commodity clusters. Its architecture supports both structured and unstructured data, allowing for immediate analysis of streaming data from IoT sensors and other sources, thereby providing timely insights and actionable intelligence. Key Features and Functionality: - Real-Time Graph Analytics: Facilitates immediate discovery of relationships and patterns within data, enabling swift decision-making. - Integration with Open-Source Technologies: Seamlessly works with Apache Spark, Kafka, YARN, and Hadoop, ensuring compatibility and scalability. - Support for Structured and Unstructured Data: Capable of ingesting and analyzing diverse data types from various sources, including IoT devices. - High-Speed Performance: Organizes data into real-world objects, eliminating inefficiencies associated with traditional relational databases and supporting data volumes beyond the petabyte level. - Parallel Ingestion and Analytics: Allows simultaneous data collection and analysis, reducing latency and enhancing responsiveness. Primary Value and Problem Solved: ThingSpan addresses the challenge of uncovering hidden relationships and patterns within vast and complex datasets in real-time. By providing a scalable and efficient platform for graph analytics, it empowers organizations to transform raw data into meaningful insights swiftly. This capability is crucial for applications requiring immediate interpretation of streaming data, such as those involving IoT sensors, where timely analysis can lead to proactive decision-making and competitive advantages. **Who Is the Company Behind ThingSpan enterprise graph and data fusion platform?** - **Seller:** [Objectivity](https://www.g2.com/sellers/objectivity) - **Year Founded:** 1988 - **HQ Location:** San Jose, US - **Twitter:** @objectivitydb (1,936 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/objectivity (21 employees on LinkedIn®) ### 16. [Vaticle](https://www.g2.com/products/vaticle/reviews) Vaticle is a team of people driven by a purpose: to solve the world's most complex problems, through knowledge engineering. We are the inventors of the Grakn knowledge-base and the Graql query language. Our technology helps organisations in various industries, including Life Sciences, Defence & Security, Financial Services and Robotics, to build intelligent systems that we believe will change the world. **Who Is the Company Behind Vaticle?** - **Seller:** [vaticle](https://www.g2.com/sellers/vaticle) - **Year Founded:** 2021 - **HQ Location:** London, GB - **Twitter:** @GraknLabs (16 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/typedb (22 employees on LinkedIn®) ### 17. [Vertexdb](https://www.g2.com/products/vertexdb/reviews) Vertex is a high performance graph database that supports automatic garbage collection, built on libevent and tokyocabinet. **Who Is the Company Behind Vertexdb?** - **Seller:** [Vertexdb](https://www.g2.com/sellers/vertexdb) - **HQ Location:** Newcastle Upon Tyne, - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 18. [xtendr](https://www.g2.com/products/xtendr/reviews) xtendr facilitates secure data sharing and collaboration between teams, departments, and organisations - generating powerful insights without ever compromising on privacy.
 Using a combination of best-in-market Privacy Enhancing Technologies (PETs), xtendr allows participants to securely share data with external analytics providers, as well as to pool their datasets in order to learn from a broader range of information - all without ever exposing personal or sensitive information. With applications in industries including healthcare, finance and manufacturing, xtendr allows collaborators to combine datasets for enhanced research, audience analysis, detection of patterns and trends, and more. **Who Is the Company Behind xtendr?** - **Seller:** [xtendr](https://www.g2.com/sellers/xtendr) - **Year Founded:** 2019 - **HQ Location:** Budapest, HU - **LinkedIn® Page:** https://www.linkedin.com/company/xtendr (10 employees on LinkedIn®) ## What Is Graph Databases? [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## What Software Categories Are Similar to Graph Databases? - [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) --- ## How Do You Choose the Right Graph Databases? ### 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.