The data ecosystem has evolved from one where it was just about effective storage and utilization of data to a modern environment where insights and analytics are instrumental components of data management. Data has now become a key ingredient that drives decision making processes. The hidden patterns have become the power-house for innovation.
Just as the meaning and purpose of data has evolved, so have the data management solutions. This is necessary in order to cater to the modern need to make meaningful use of not just data, but massive amounts of data in the hands of organizations.
Vector databases and graph databases are among the innovative database management technologies that have emerged to cater to the rising needs of the modern data ecosystem. Both have proven to be pivotal in providing unique advantages that enable us to derive immense value out of data.
In this guide, we compare these two with the objective of helping you distinguish and select the best option for your project's needs.
For a deeper look into choosing databases for projects, dive into how to choose a database for your specific project.
What is a vector database?
As the name suggests, a vector database is one that is designed to store data in the form of vectors. What are vectors? Vectors are mathematical representations of information as an array of numbers. The numbers capture various attributes or features of the data. For instance, in machine learning, vectors often represent data points such as images, text, or audio. This is achieved by converting the data points into numerical forms through processes like embedding.
Each point in a vector graph represents a single piece of data. The location of the point indicates the attributes that relate it to other points. Points with similar attributes are located closer to one another, while those with negligible similarities are located further.
This unique structure is what makes vector databases suitable in areas such as similarity search. Since the vectors are organized based on similarity, it means that it is quite easy to locate data points that are similar to a user's query vector.
The vectors, or data points, in vector databases are created by transforming raw data into a high-dimensional vector space through a process known as embedding. This is made possible with the help of Machine Learning models, which generate the embeddings. These embeddings are then stored in the vector database to enable efficient analysis and querying.
Essentially, vector databases are designed for purposes of storing and querying data that contains many attributes. They are extremely good at showing the similarities that exist in data that is highly dimensional. The closeness of vectors is measured by using distance metrics. The closer the vectors, the more the similarity.
For demonstration purposes, let's use the example of a video loving person. They document their trips in videos. The videos have so many attributes such as locations, times, friends, colleagues, etc. Now assume you have so much of this kind of data, comprising video folders from millions of users. If you wanted to find videos of best friends or colleagues, it would take so much time. In fact, let's just say that it would be impossible to do it manually or even effectively with the traditional databases. But with a vector database, the folders are stored in a vector database and all you will need to do is tell the database to look for videos of best friends.
Generally, vector databases support batch processing as well as real-time ingestion of data. Because of this, they are very popular in applications that demand the support of real-time processes in swift succession. Think of customer support chatbots that need to give answers to customer queries in real-time.
What is a graph database?
A graph database is one that is designed to represent and store data in terms of nodes, edges, and properties. Collectively, these form a graph structure. Graph databases are optimized for handling complex relationships that are interconnected.
Nodes represent entities (such as people or objects)
Edges represent the relationships between the entities
Properties provide additional information about the nodes and edges.
This structure enables efficient querying and analysis of relationships and patterns within large and complex datasets. It is this strength that makes graph databases particularly useful for applications such as social networks, recommendation systems, and network analysis.
If you are new to the concept of graph databases, please have a look at our comprehensive graph database guide.
Comparing vector databases with graph databases — features
Having looked at the key definitions of these two powerful databases, let's now dig deep into the main items that reflect the core comparison between the two:
1. Querying
Vector databases are great when you want to apply them in use cases where similarity is of critical importance. This is why they are popular in applications like image retrieval, where it's important that content similarity is taken into account.
Graph databases are efficient in querying data that has humongous relationships with deep connections. This is why these databases are preferred for applications such as social networks and recommendation engines especially for eCommerce platforms. They are also powerful when it comes to navigating knowledge graphs.
2. Scalability
Overall, vector databases scale well and can work comfortably with large volumes of data. It's also optimized to cale efficiently across various algorithms that focus on similarity search.
By nature, graph databases are schema-less. This makes it easy to add and modify data.
3. Data representation
As indicated in definition, vector databases organize data in a structure that is made up of points, spread in a vast space that is multi-dimensional. This structure makes the vector databases ideal for capturing deep similarities within the data. In short, the representation in vector databases is such that it promotes easy retrieval of similar elements.
In graph databases, data representation is structured in the form of interconnected nodes that are linked by the relationships that exist between them. This structure makes it possible to derive useful insights hidden in the data, through analytics.
Comparing vector databases with graph databases — use cases
This is probably the heart of any comparison between vector databases and graph databases. Because in the end, it comes down to use cases. What is it that your project needs to achieve, and what database among these two is best suited for the task?
Here, we look at the most prominent use cases and where each of these databases excels.
1. Fraud detection
The role of vector databases in fraud detection is to identify similarities in transactions and the people involved in those transactions. For example, you can use a vector database to analyze patterns in credit card transactions by mapping each transaction as a vector. This allows the system to identify unusual transaction patterns or similarities between different accounts. Take the example of multiple accounts making purchases from the same IP address or buying similar items in the same locations.
Want to uncover unscrupulous actions hidden in complex data involving many interconnected individuals? Graph databases are great in this use case. In fact, they have enabled financial institutions and governments to uncover massive cases of financial fraud such as money laundering. For example, a graph database can map the relationships between different bank accounts, transactions, and individuals. This will then make it easier to detect suspicious patterns. It can identify a network of accounts that frequently transfer money among each other in circular patterns — a common tactic in money laundering.
Learn more about a worrying rend of fraud targeting top executives, known as CEO Fraud.
2. Entertainment
In media and entertainment use cases, vector databases are powerful in analyzing features such as topics for articles or even music genres. With this, the systems can then easily recommend similar content and this enables the applications to serve the preferences of so many different categories of individuals. For example, a news website can use a vector database to recommend articles to readers by analyzing the topics and sentiments of the articles they've previously read. Similarly, a music streaming service can leverage vector databases to suggest new songs or artists to listeners by identifying the underlying characteristics of the music they enjoy, such as genre, tempo, and instrumentation.
Graph databases, on the other hand, can help to navigate the relationships in content. Some of the features that can be analyzed include social shares, watch lists, etc. For example, a streaming service can use a graph database to analyze how viewers are connected based on their viewing habits and social interactions. Once these relationships are mapped, the service can recommend new shows or movies to users based on what their friends are watching. They can also track trending content within different social circles and identify influencers who can help promote new releases.
For example, the giant social network, Facebook, utilizes a graph database to recommend new friends to users based on relationships such as interests and friends that are already connected to the user.
3. Scientific discovery
Vector databases can analyze complex scientific factors such as gene expressions to identify significant similarities that can unearth life-changing discoveries. For example, researchers can use a vector database to compare genetic data from different populations to find common genetic markers associated with specific diseases. This can lead to the identification of new drug targets or the development of personalized medicine approaches. This makes it possible for treatments to be tailored to an individual's genetic makeup.
Graph databases can be effective in helping to navigate relationships in molecular interactions. For example, researchers can use a graph database to map out the relationships between different proteins within a cell. Through this, key proteins can be identified to determine the roles they play in disease pathways. This can accelerate the development of new treatments and enhance the comprehension of cellular functions.
4. Ecommerce
Ecommerce is huge on databases. This is precisely because eCommerce is indeed one of the top industries that rely on massive data to drive sales and product innovations. From product recommendations to organizing massive catalogs, ecommerce can benefit greatly from both vector databases and graph databases.
In ecommerce, vector databases can analyze product attributes to establish similarities and thereafter recommend products that are similar to those a customer has bought before. For example, an online fashion retailer can use a vector database to recommend clothing items to customers based on similar factors such as styles, colors, and materials of the items. The point is to analyze these attributes, then suggest similar or complementary products. This enhances the shopping experience.
Graph databases can identify the relationships that exist between the many products that eCommerce platforms and systems handle. Some of the relationships come from items such as wish lists. For example, by using a graph database, it is possible to use wish lists to capture deep relationships between different customers. These relationships can be utilized further to inform the introduction of specific products that target a specific group of customers.
Vector Database vs. Graph Database: Choosing the Right Option for Your Project
Now that we have seen the differences, here is still that ever critica question of, how do you best choose the right database for your project when faced with the two options of vector database on one hand and graph database on the other hand.
1. Get a deep grasp of your data
Your data is probably already complex, the more the reason you need to use either of these two databases. So, the first step is to dive deep and understand the data.
Here, you essentially want to look at the architecture of the data. This simply comes down to factors such as structured or unstructured data, existence of relationships or independent data points, etc.
Another critical factor to think about is scalability. How quickly do you anticipate the data to grow?
2. What is the use case?
Where exactly do you want to deploy the database solution? What is the end objective? Product recommendations for an eCommerce website? Fraud detection in a bank? Knowledge Graph analysis?
There are so many use cases, and many of them can be tackled with modern innovative databases.
3. Consider the desired performance
Some applications require super high scale performance capabilities such as real-time responses. For this level of performance, it means that the queries will be expected to be quite complex, and the database should be capable of handling this.
Advantages and disadvantages
As you may have noticed, both vector databases and graph databases can be used in many applications. Having looked at the applications and use cases we highlighted, you may be left wondering about the exact advantages or disadvantages that would indeed help you to make the right choice. Let's highlight some of the standout advantages and disadvantages of both.
Vector databases: advantages
Very powerful in handling complex, high-dimensional data such as audio files, images, etc.
Very efficient in executing searches that entail queries that seek similar data points.
Very flexible when it comes to accommodating Machine Learning models.
Vector databases can improve user engagement by powering the discovery of patterns that enable businesses to drive initiatives such as personalized experiences.
As the size of the data expands, it's easy to scale while maintaining high performance.
The ability to work flexibly with Machine Learning models means that vector databases can be deployed efficiently in automation and applications that drive decision-making.
Vector databases: disadvantages
As dimensionality increases, it's possible that the efficiency of searches can decrease.
Accuracy and precision can be diluted in the pursuit of speedy searches
Large datasets mean high dimensional vectors. This can exert pressure on memory requirements.
High computing power is needed. To navigate this downside, vector database solutions often use algorithms such as the Approximate Nearest Neighbor when executing searches in huge datasets.
Graph databases — disadvantages
Graph databases are the king of managing data with complex, connected relationships.
Graph databases are not only fast but also very flexible. This makes them suitable for use cases where constant change in needs is inevitable. What this means is that it's so easy to adapt quickly to changing needs by integrating new data or adjusting the current data.
As the query times are fast, decisions can be made faster to drive greater growth and revenues.
Graph databases — disadvantages
The initial graph databases experienced challenges when it came to scaling easily across multiple nodes. But modern graph databases are quickly overcoming this challenge with innovative scaling features.
Query languages for some graph databases can present a steep learning curve for new users, especially those that are making the transformation from the traditional relationship databases.
Harnessing the power of modern databases!
Remember the period when analysts used to predict that data will be the new oil. The prediction has now come to pass and the moment of harnessing is here with us.
It is this exact realization that has necessitated the aggressive innovation that we are witnessing around the area of database management systems.
Thanks to these innovations and the birth of modern database management solutions, you no longer need to struggle with the bottlenecks of traditional database systems.
Vector databases and graph databases are just part of the larger vast ecosystem of modern database solutions in your disposal. Choose the most suitable according to your needs, and witness exponential transformation of your data to a gold-mine of opportunities.
It's also important to note that you can actually combine a graph database and a vector database to achieve the desired objective. This means you take advantage of each database's strengths while switching up the weaknesses. This has even been made easier by some database providers who are incorporating some features that their database solutions lack. For example, Neo4j added a feature that makes it possible to conduct vector similarity checks.
Before you leave, you may want to have a look at relational databases vs. graph databases.
