Graph databases are rapidly growing, and most organizations are turning to them as their preferred database management systems. According to statistics released by DB Engine, a website that ranks database management solutions, graph databases have been steadily growing in popularity since 2013. Graph databases started becoming popular when many organizations started adopting them.
Interestingly, the growth of graph databases has surpassed any other form of a database management system. Projections show that the global graph database market will increase to $11.25 billion by 2030. The need for flexible online schema environments will fuel this massive growth.
Graph Database Elements
A graph database has two elements—a node and a relationship. The node represents an entity, which can be a person, item, place, category, or any other piece of essential data. On the other hand, each relationship represents the connection between the nodes.
This structure allows a user to model any type of scenario—from a network of devices to a system of roads, a person’s medical history, purchase history, or any other thing that can be defined by a relationship.
Why Should You Consider Using a Graph Database?
Companies like Facebook, Amazon, Netflix, and Google have all tapped into the power of graph databases to increase their competitive advantage. Their strategy involves utilizing the technology behind graph databases to maximize the potential of data connections. This has been a key factor in their success.
Graph databases are purpose-built to manage highly-connected data. Moreover, the ever-growing volume and connectedness of modern data give companies that have adopted graph databases an upper hand over their competitors.
Now that you understand what is a graph database, here are the key advantages of using graph databases.
Flexible Data Modeling
Graph databases offer flexible data modeling. That means the data can be easily structured and connected in ways that make sense for a specific business use case.
In a graph database, data is stored as nodes and relationships between those nodes. This makes it possible to create complex and interconnected data models that can capture the relationships between data entities. For example, a recommendation system’s graph database can store information about users, items, and their relationships, such as ratings, purchases, and views. This allows for a more nuanced understanding of user behavior and preferences and can lead to more accurate and relevant recommendations.
Additionally, the flexible data modeling offered by graph databases allows easy data structure modification to adapt to changing business requirements. For example, if a new type of data needs to be added, it can be easily incorporated into the existing graph without disrupting the current data structure. This flexibility can save time and effort in the development and maintenance of the database and enable faster and more agile innovation in the business.
Graph databases are designed to scale horizontally. This allows them to handle large volumes of data by adding more nodes to the cluster. Because of that, graph databases are highly scalable, making them well-suited for applications that require high performance and scalability, like recommendation systems.
Most times, scaling up can be challenging and expensive in a traditional relational database. Typically, this involves upgrading the hardware and software of the database server, which can be time-consuming and costly. Furthermore, as the size of the database grows, the performance of the database can degrade, leading to slower queries and longer response times.
In contrast, graph databases are designed to scale out, meaning new nodes can be added to the cluster to handle the additional load. This approach allows graph databases to scale horizontally, which is much more cost-effective and easier to manage than scaling up a traditional relational database.
The horizontal scalability can be achieved by partitioning the graph database across multiple nodes in the cluster. Each node in the group can store a subset of the data, and queries can be distributed across the set to take advantage of the parallel processing power of the nodes. As the size of the database grows, additional nodes can be added to the cluster to handle the extra load.
Graph databases’ high performance stems from how they store and query data. Unlike traditional relational databases, where data is stored in tables with rows and columns, graph databases store data as nodes and relationships between nodes.
That means data can be modeled more flexibly and intuitively. Moreover, queries can be expressed in a declarative language that allows for efficient traversal of nodes and relationships.
Because of this unique data model and query language, graph databases are highly optimized for fast and efficient querying of interconnected data. They can handle complex queries over large datasets in real time, which is critical for applications such as recommendation systems, fraud detection, and social networks.
This high performance is achieved through a combination of factors. These include efficient data storage and indexing, optimized query processing, and the use of specialized algorithms for graph analysis. Thus, graph databases can easily handle large volumes of data and complex queries, making them a powerful tool for building real-time, data-driven applications.
A graph database can easily integrate data from multiple sources. This flexibility makes it easier to integrate data from disparate sources, such as social media, sensor data, and transactional data.
By integrating data from multiple sources, a graph database can provide a more holistic view of the data, leading to more accurate and relevant recommendations. It also allows for easier data exploration and analysis, as all data is stored in a single location and can be queried using a common language.
Furthermore, graph databases can provide real-time data integration, which means that data can be updated and accessed in real-time as new data becomes available. This is particularly useful for recommendation systems, as they require real-time data to provide accurate and relevant recommendations.
The ability of graph databases to integrate data from multiple sources allows for a more comprehensive and accurate understanding of the data, which can lead to more effective and impactful business decisions.
Graph databases are designed to store interconnected data in a way that ensures that relationships between data entities remain valid, even as data is updated or deleted. This happens by using a concept called referential integrity. The concept ensures that relationships between data entities remain valid.
Referential integrity means that a relationship between two nodes can only exist if both nodes exist in the database. If a node is deleted or modified in a way that would break a relationship, the database automatically updates or deletes the corresponding relationships to maintain integrity.
This can be particularly important in applications such as recommendation systems, where accurate and up-to-date data is essential for generating relevant and valuable recommendations. In addition, it can help prevent data inconsistencies and errors that can arise in more traditional databases that rely on joins and foreign key constraints to maintain data integrity.
Unlike traditional databases, graph databases organize and store data in a way that emphasizes the connections and relationships between different entities. This ensures a more flexible and efficient modeling of complex relationships between various system parts.
And as a result, graph databases get better problem-solving capabilities. For example, graph databases can handle complex queries involving many-to-many relationships more efficiently than traditional ones. This makes it easier to analyze data in a sophisticated way and get more accurate results.
Graph databases are also optimized for real-time analysis of complex, connected data. That means they can quickly identify and resolve problems as they occur, which is crucial for many organizations. Moreover, graph databases are designed in a manner that allows them to provide a more natural way to model complex data structures. This makes it easier for a user to see and understand the relationships between different entities in a system, which can lead to better problem-solving.
Also, graph databases come with built-in visualization tools that allow users to see the relationships between different entities in a system. This can be very helpful in identifying patterns and connections that might not be immediately apparent in a traditional database.