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Graph Databases vs. Vector Databases: What Is Better for Your Project?

Although vector and graph databases might seem similar at first glance, the differences between them are much bigger than you might initially think. Among other things, they differ in terms of data retrieval and analysis, data structure, queries, and performance. As you can gather from their names, graph databases use graphs for managing data, while vector databases rely on vectors.

When choosing a database solution for your business, you should first consider your business needs, the characteristics of your data, and the company use cases. By finding the right system for your particular requirements, you can squeeze maximum value from every software dollar.

Before explaining the pros and cons of each system, as well as their ideal use cases, let’s first define these two terms.

What are graph databases?

The main characteristic of a graph database is that it uses a graphical structure to store company data. Everything within this database is categorized by using a combination of vertex and edges. While vertices represent large categories of data, edges are utilized to establish relationships between these bigger entities.

The best thing about graph databases is how they process relationships. With this software, you can easily handle larger sets of data and determine connections between nodes. These systems excel at finding paths, identifying various patterns, and traversing relationships, which makes them perfect for making complex business decisions.

While there are numerous use cases for graph databases, they’re especially popular for recommendation systems, social networks, business knowledge graphs, and fraud detection. One of the best examples of a graph database is NebulaGraph, which is open-source software with unlimited scalability and minimal latency.

What are vector databases?

In this particular case, all data is stored within vectors that have a specific number of dimensions. You can use them for just about any type of unstructured or structured data, including text, images, and audio, making them extremely versatile. Experts praise this software for its scalability and performance compared to traditional systems.

Vector databases are growing in popularity as of late, as they provide lots of benefits for AI-driven systems. These databases can quickly establish similarities between data and detect closest neighbors. As such, they are perfect for any type of browsing, recommendations, clustering, and anomaly detection.

Some of the best examples of vector databases are Milvus and Pinecone.

Graph database pros and cons

Graph databases are renowned for their speed and flexibility. A company can utilize them in all sorts of ways, and as such, they're almost a must-have for any modern business.

Pros

  • Flexibility and quickness – Graph databases provide a lot of flexibility for your day-to-day operations. You can integrate these systems with various sources, adapt them for new use cases, and ensure that all your data is in accordance with business goals
  • Improved training – Many companies use graph databases to improve their training and onboarding. The software allows the creation of efficient knowledge bases that can serve as a company-wide information source
  • Enhanced protection – In financial risk control use cases, graph databases are ideal for detecting fraud rings and other sophisticated scams as it can unlock the highly complex correlation in fraudulent transactions
  • Better decision-making – Graph databases are ideal for making business decisions. This type of software can establish relationships between different data groups, which makes it easier to determine the right moves for your company
  • Better for complex queries – One of the best things about graph databases is that they allow you to process intricate queries by establishing cycles and discovering shortest paths

Cons

  • Issues with scalability – Except for NebulaGraph, most graph databases have problems with scalability. Luckily, many software companies are slowly working on resolving this issue and providing users with increased value
  • Additional overhead – Certain datasets won't benefit from this technology. Basically, in certain cases, you don't need to establish relationships between different data categories, making your graph database solution redundant
  • Steep learning curve – This type of software requires knowledge of complex query languages such as Neo4j and Cypher, which require more time to master

Vector database pros and cons

In many ways, vector and graph databases are polar opposites. Vector solutions are ideal for processing search queries and can easily be used for machine learning models.

Pros

  • Content flexibility – These databases can process any type of data, including text, images, and audio
  • Machine learning benefits – The software can easily be integrated with ML models, making it ideal for modern business and marketing challenges
  • Improved similarity searches – Vector databases can easily find data points that are close to each other within multi-dimensional space. As such, it is a perfect tool for determining differences and similarities between various data points
  • Improved automation – Given that machine learning is vital for automation, vector databases can, by proxy, automate your business processes
  • Enhanced scalability – Vector systems allow you to scale the processes as much as you want without ever encountering issues with availability or speed

Cons

  • Lower accuracy – Due to the speed of certain types of data retrieval, there are cases where vector databases provide subpar accuracy
  • Issues with dimensionality – As the dimensionality increases, you’ll notice a drop-off in search efficiency and data availability. Although the creators use certain techniques that would mitigate the problem, it’s still a notable issue for this type of software
  • High requirements – Vector databases are notorious for having high storage and memory requirements. This is especially true if you’re handling large datasets or as you start scaling the business

Combining graph database and vector database

Although combining the two types of software might be tricky, and some companies might even consider it gimmicky, there are lots of benefits to it. Besides giving you more versatility, the use of vector and graph databases can ensure better query options, richer data representation, improved recommendations, and a unified data system. Whatever the case, you need to study all available solutions before committing to one of them or using them in conjunction. That way, you can maximize your software expenditures while leaving competitors behind.