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Products classified in the overall Graph Databases category are similar in many regards and help companies of all sizes solve their business problems. However, small business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Small Business Graph Databases to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Small Business Graph Databases category.
In addition to qualifying for inclusion in the Graph Databases category, to qualify for inclusion in the Small Business Graph Databases category, a product must have at least 10 reviews left by a reviewer from a small business.
Neo4j is the leading native graph database and graph platform. It is available a both open source and through a commercial license for enterprise levels of security and high performance and reliability through clustering. Neo4j's graph query language, Cypher is very easy to learn and can operate across Neo4j, Apache Spark and Gremlin-based products using newly released open source toolkits, "Cypher on Apache Spark (CApS) and Cypher for Gremlin. Neo4j also offers a complete graph platform that
One database. One Query Language. Three data models. Endless Possibilities. With more than one million downloads, ArangoDB is a fast growing native multi-model NoSQL database. It combines the power of graphs, with JSON documents and a key-value store. ArangoDB makes all of your data-models accessible with a single elegant declarative query language. ArangoDB is the simple, versatile and performant answer to many challenges facing developers, startups and enterprises in the near and far future. S
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 mak
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.
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
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.
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 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.
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.