Best Graph Database Solutions

What are Graph Databases?

Why Use Graph Databases?

Who Uses Graph Databases?

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 — 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 — 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.