Best Graph Database Solutions

How Many Graph Databases Products Does G2 Track?

Total Products under this Category: 71

Category Stats (Sep 2026)

  • Average Rating: 4.49/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Elastic Stack (+0.09%) - Among all products in this category, Elastic Stack recorded the largest rating increase compared to last month

Last updated: September 15, 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
  • 71+ 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.

G2 Grid® for Graph Databases

G2 Grid® for Graph Databases plotting products by satisfaction and market presence

Highlighted products: Arango, Amazon Neptune, Elastic Stack, Neo4j Graph Database, GraphJSON, OrientDB, Stardog, and FlockDB.

Underlying data: [Grid® JSON](https://www.g2.com/categories/graph-databases/grids.json?focus%5B%5D=arango&focus%5B%5D=amazon-neptune&focus%5B%5D=elastic-stack&focus%5B%5D=neo4j-graph-database&focus%5B%5D=graphjson&focus%5B%5D=orientdb&focus%5B%5D=stardog&focus%5B%5D=flockdb)

Arango

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

How Do G2 Users Rate Arango?

  • Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Arango?

  • Seller: Arango
  • Year Founded: 2015
  • HQ Location: San Francisco, CA
  • LinkedIn® Page: www.linkedin.com
    128 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Senior Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 57% Small, 23% Medium

What Do G2 Reviewers Say About Arango?

AI-generated summary from verified user reviews

Pros
  • Users find Arango remarkably easy to use, appreciating its intuitive interface and quick learning curve for AQL.
  • Users value Arango's seamless data handling, combining graph, document, and key-value seamlessly with excellent documentation.
  • Users appreciate the straightforward query language of Arango, making complex queries accessible and easy to learn.
  • Users praise ArangoDB for its intuitive query language, making it easy for new developers to learn and use effectively.
  • Users value the customization potential of Arango DB, appreciating its flexible query language and versatile design.
Cons
  • Users find the poor usability of ArangoDB challenging, citing complexity in operations and setup as major issues.
  • Users find the difficult learning process challenging, particularly with complex queries and limited community support.
  • Users find the improvement needed for the graphical interface and setup process, wishing for a more intuitive experience.
  • Users find the learning curve steep, especially for complex queries and building new databases with limited support.
  • Users find the steep learning curve challenging, particularly for building complex queries and databases.

What Are Recent G2 Reviews of Arango?

What Are G2 Users Discussing About Arango?

Amazon Neptune

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: 4.3/5.0

Total Reviews: 31

How Do G2 Users Rate Amazon Neptune?

  • Has the product been a good partner in doing business?: 8.5/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Amazon Neptune?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 65% Small, 26% Large

What Are Recent G2 Reviews of Amazon Neptune?

Elastic Stack

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: 104

How Do G2 Users Rate Elastic Stack?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Elastic Stack?

  • Seller: Elastic
  • Year Founded: 2012
  • HQ Location: San Francisco, CA
  • Twitter: @elastic
    65,200 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    10,457 employees on LinkedIn®
  • Ownership: NYSE: ESTC

Who Uses This Product?

  • Who Uses This: Software Engineer, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 47% Medium, 34% Large

What Do G2 Reviewers Say About Elastic Stack?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Elastic Stack, enabling quick correlation and understanding of system behavior.
  • Users value the flexibility of Elastic Stack for its customization, integration, and deployment options across various environments.
  • Users appreciate the unified log management that allows for quicker issue correlation and comprehensive system understanding.
  • Users praise the fast and scalable real-time search of Elastic Stack, enhancing their ability to analyze data efficiently.
  • Users value the versatility of Elastic Stack, seamlessly integrating various tools for flexible data management and visualization.
Cons
  • Users note the resource management challenges in Elastic Stack, requiring expertise to maintain performance and stability.
  • Users find the complexity issues of Elastic Stack demanding, requiring significant expertise to manage effectively and maintain performance.
  • Users find the cost of resources for Elastic Stack to be significant, especially at scale and for advanced features.
  • Users find that High Memory Usage in Elastic Stack can lead to increased costs and management complexities at scale.
  • Users find the steep learning curve of Elastic Stack challenging, requiring significant experience for effective management and optimization.

What Are Recent G2 Reviews of Elastic Stack?

What Are G2 Users Discussing About Elastic Stack?

Neo4j Graph Database

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

How Do G2 Users Rate Neo4j Graph Database?

  • Has the product been a good partner in doing business?: 8.8/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Neo4j Graph Database?

  • Seller: Neo4j
  • Year Founded: 2007
  • HQ Location: San Mateo, CA
  • Twitter: @neo4j
    47,112 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1,063 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 44% Small, 30% Medium

What Do G2 Reviewers Say About Neo4j Graph Database?

AI-generated summary from verified user reviews

Pros
  • Users appreciate how Neo4j's natural modeling of complex relationships optimizes queries for interconnected data efficiently.
  • Users appreciate the design flexibility of Neo4j, enabling efficient modeling of complex relationships in interconnected data.
  • Users appreciate the ease of use with Neo4j, valuing its intuitive representation of complex relationships in data.
  • Users appreciate the intuitive modeling of complex relationships in Neo4j, enhancing data exploration and query efficiency.
  • Users appreciate the flexibility of Neo4j, enabling efficient representation and querying of complex, interconnected data.
Cons
  • Users face backup issues with Neo4j, finding backup and restore processes complex for extensive historical data management.
  • Users face data management issues with Neo4j, particularly with backup complexity and importing data from various sources.
  • Users find learning Neo4j's complex Cypher query language and backup processes challenging, especially transitioning from SQL backgrounds.
  • Users face import issues with Neo4j, complicating data transfers from structured sources like Wiki pages.
  • Users face a challenging learning curve with Neo4j, especially transitioning from SQL and managing complex queries.

What Are Recent G2 Reviews of Neo4j Graph Database?

What Are G2 Users Discussing About Neo4j Graph Database?

GraphJSON

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

How Do G2 Users Rate GraphJSON?

  • Has the product been a good partner in doing business?: 8.7/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind GraphJSON?

  • Seller: GraphJSON
  • HQ Location: N/A
  • Twitter: @GraphJSON
    513 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 54% Small, 37% Medium

What Are Recent G2 Reviews of GraphJSON?

OrientDB

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

How Do G2 Users Rate OrientDB?

  • Has the product been a good partner in doing business?: 7.8/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind OrientDB?

  • Seller: SAP
  • Year Founded: 1972
  • HQ Location: Walldorf
  • Twitter: @SAP
    297,052 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    149,349 employees on LinkedIn®
  • Ownership: NYSE:SAP

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 47% Small, 42% Medium

What Are Recent G2 Reviews of OrientDB?

What Are G2 Users Discussing About OrientDB?

Stardog

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

How Do G2 Users Rate Stardog?

  • Has the product been a good partner in doing business?: 8.7/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Stardog?

  • Seller: Stardog Union
  • HQ Location: Arlington, VA
  • Twitter: @StardogHQ
    3,964 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    89 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 39% Small, 33% Large

What Are Recent G2 Reviews of Stardog?

FlockDB

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

How Do G2 Users Rate FlockDB?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.9/10)

Who Is the Company Behind FlockDB?

  • Seller: Twitter
  • Year Founded: 2006
  • HQ Location: San Francisco, CA
  • LinkedIn® Page: www.linkedin.com
    966 employees on LinkedIn®
  • Ownership: NYSE: TWTR
  • Total Revenue (USD mm): $3,716

Who Uses This Product?

  • Company Size: 36% Medium, 36% Small

What Are Recent G2 Reviews of FlockDB?

What Are G2 Users Discussing About FlockDB?

Dgraph

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

How Do G2 Users Rate Dgraph?

  • Has the product been a good partner in doing business?: 9.8/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Dgraph?

  • Seller: Dgraph Labs
  • Year Founded: 2016
  • HQ Location: San Francisco, CA
  • Twitter: @dgraphlabs
    16 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    19 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services
  • Company Size: 68% Small, 18% Large

What Are Recent G2 Reviews of Dgraph?

What Are G2 Users Discussing About Dgraph?

GraphQL

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

How Do G2 Users Rate GraphQL?

  • 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)

Who Is the Company Behind GraphQL?

Who Uses This Product?

  • Company Size: 64% Medium, 36% Small

What Are Recent G2 Reviews of GraphQL?

What Are G2 Users Discussing About GraphQL?

Tigergraph

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

How Do G2 Users Rate Tigergraph?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind Tigergraph?

  • Seller: Tigergraph
  • Year Founded: 2012
  • HQ Location: Redwood City, CA
  • Twitter: @TigerGraphDB
    12,646 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    142 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 55% Large, 36% Medium

What Are Recent G2 Reviews of Tigergraph?

What Are G2 Users Discussing About Tigergraph?

Cayley

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

How Do G2 Users Rate Cayley?

  • Has the product been a good partner in doing business?: 6.7/10 (Category avg: 8.9/10)

Who Is the Company Behind Cayley?

  • Seller: Cayley
  • HQ Location: N/A
  • Twitter: @cayleygraph
    730 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 45% Small, 36% Medium

What Are Recent G2 Reviews of Cayley?

What Are G2 Users Discussing About Cayley?

GraphBase

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

How Do G2 Users Rate GraphBase?

  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.9/10)
  • 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)

Who Is the Company Behind GraphBase?

  • Seller: FactNexus
  • Year Founded: 2010
  • HQ Location: Sydney
  • Twitter: @AskKayBot
    5 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 50% Small, 38% Medium

What Are Recent G2 Reviews of GraphBase?

Redis Cloud

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

How Do G2 Users Rate Redis Cloud?

  • Has the product been a good partner in doing business?: 9.2/10 (Category avg: 8.9/10)

Who Is the Company Behind Redis Cloud?

  • Seller: Redis
  • Year Founded: 2011
  • HQ Location: San Francisco, CA
  • Twitter: @Redisinc
    44,002 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1,542 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 50% Small, 41% Medium

What Are Recent G2 Reviews of Redis Cloud?

What Are G2 Users Discussing About Redis Cloud?

EdgeDB

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

How Do G2 Users Rate EdgeDB?

  • 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)

Who Is the Company Behind EdgeDB?

  • Seller: EdgeDB
  • Year Founded: 2019
  • HQ Location: San Francisco, US
  • LinkedIn® Page: www.linkedin.com
    24 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 67% Small, 22% Medium

What Are Recent G2 Reviews of EdgeDB?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated October 8, 2024

Graph Databases Topics

Expand/Collapse

Learn More 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.

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.