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Database technologies have been expanding for decades, and there is no shortage of options. But, while relational databases are a staple across industries, NoSQL database adoption has grown recently.
One of the top choices for NoSQL is a graph database, with enterprise adoption trending for several years now as organizations work to answer increasingly complex questions using complex data. Graph processing and graph database applications are projected to grow 100% annually through 2022, according to Gartner.
As graph database adoption continues to grow, it's important to understand the differences between a graph database vs. relational database. Each offers different advantages of data models and abilities to connect relationships between data.
What is a graph database?
A graph database is a NoSQL database that stores data as a network graph. What differentiates graph databases from other options is that they document and prioritize the relationships between data.
Graph databases are made up of nodes and edges, where nodes represent specific entities, while edges represent the connection between two nodes. They are designed to be scalable and offer flexibility that's hard to find in other databases.
What is a relational database?
Relational databases store data in relational tables. Tables are defined by columns and rows, and each row is identified by a unique key so they can be linked to rows in other tables.
Each individual table also includes a primary key identifying the information found within the table. For example, one table may contain customer information that relates to information in a different table containing order information.
Relational databases separate the logical structures of tables and indexes from physical storage structures. This enables data professionals to make changes to physical data structure that don't affect the logical structure.
Graph database vs. relational database
The most notable difference between the two is that graph databases store the relationships between data as data. Relational databases infer a focus on relationships between data but in a different way. The relational focus is between the columns of data tables, not data points.
Both databases make adding new data easy. The flexibility of a graph database enables the ability to add new nodes and relationships between nodes, making it reliable for real-time data. Relational databases make adding new tables and columns possible while the database is running.
Complex queries typically run faster in graph databases than they do in relational databases. Relational databases require complex joins on data tables to perform complex queries, so the process is not as fast.
When to use each database
Both types of databases offer advantages to users, but deciding between graph database vs. relational database adoption comes down to how the database will be used.
According to IBM, relational databases are the most popular query tool across businesses. The tabular structure makes them a good choice for records with uncomplicated relationships. For example, relational databases are popular for accounting and other transactional data because straightforward data with established relationships fits easily into the relational database format.
Graph databases' added emphasis on relationships helps explore complex data sets. This can help data professionals find unexpected connections between data points, making them a top choice for relationship-heavy use cases. Common use cases for graph databases include social media, fraud detection and recommendation engines. Mapping relationships also makes graph databases a good fit for data visualizations.
Organizations may also benefit from using both types of databases. Both graph databases and relational databases prioritize connections, but how those relationships are structured gives advantages or disadvantages for different applications. Choosing between a graph database vs. relational database comes down to evaluating what each application needs.