Why Use a Graph Database? Benefits Of Graph Databases
Graph databases are growing at an impressive pace, increasingly becoming the go-to DBMS for a growing number of large organizations. DB Engine, a website dedicated to the ranking of database management solutions, shows that graph databases have experienced consistent popularity since 2013 when many companies started appreciating them. The growth actually surpasses all other forms of DBMS. The worldwide graph database market is expected to increase to 11.25 Billion by 2030, rising from 1.59 Billion in 2020. This enormous surge is driven by the high need for elastic online schema environments.
Why choose a graph database for your business
You should choose graph databases because they perform way better at scale compared to other DBMS. They make it possible to represent data in a much more intuitive way, a versatile way of visualizing data which enhances query performance and enables businesses to make valuable decisions like never before. They are also less intense in terms of computational demands.
This is important because for many businesses, the ability to store, visualize, query, and analyze data at a deeper level is key to success these days. Traditional databases can maintain large amounts of data and are capable of running complex queries, yet they rely on rigid and often complex schemas. An alternative solution is a graph database that stores information as interconnected points. The interconnected structure helps to connect objects according to specific criteria, allowing data teams to find themes or patterns within the data more easily than with a traditional approach.
Queries in a graph database run much faster, as they don't have to traverse entire tables or deal with foreign key constraints. In addition, since relationships between entities are stored, the data remains consistent even when it changes over time.
Benefits of graph databases
The key benefit of graph databases is the ability to handle big data that is deeply interconnected. As we are right in the middle of the data era, this one perk will prove invaluable to businesses that are focused on boosting their success through big data.
Here are the comprehensive benefits of graph databases:
1. High performance
Graph databases allow users to query linked data quickly and efficiently, making them much faster than traditional relational databases. With sophisticated algorithms and powerful search functions, graph databases can return complex query results across an interconnected network in milliseconds.
Querying in traditional databases can become slow very quickly when dealing with more complicated and larger data sets. Conversely, the performance of graph databases will remain consistent, no matter how large or complex the data is.
For example, NebulaGraph can so easily store and process graphs with trillions of edges and vertices - the only open source graph database that is capable of doing this!
Also read: NebulaGraph joins Data Benchmark Council
2. Data integrity
Graph databases are designed to maintain the integrity of data connections across a wide variety of data sources and formats. By connecting disparate pieces of information in a graph, organizations can ensure that their data remains consistent and accurate throughout their applications.
Instead of simply storing individual pieces of information in a database, graph databases link related data together using connected nodes and edges. This allows users to easily visualize highly connected data and establish how any changes to the data will affect other areas, hence enhancing data integrity.
3. Enhanced contextual awareness
Because graph databases store data in the form of relationships between objects, they enable users to uncover nuances in interactions that may not be evident at first glance with traditional techniques such as tabular relational models or hierarchical tree structures.
This enhanced contextual awareness helps organizations make better decisions by leveraging high level insights from their data. This is a crucial benefit in the modern business landscape where there is far too much data that is just idle. Graph databases can mine this data and deliver transformative insights to spur organizational growth.
The schema and structure of the graph model is incredibly adaptable and can keep up with the high pace of ambitious data teams. There's no need to go through the long-winded process of modeling domains; just add to the current structure without disrupting its existing functionality.
Traditional database technologies often reach a scalability bottleneck when trying to bridge multiple sources of data. But graph technologies avoid this issue entirely with their distributed nature which allows more flexible scaling up or down as needed.
NebulaGraph provides linear scalability, enabling users to expand the cluster with additional nodes or services without interfering with performance.
Today's development landscape is rapidly evolving. It's all about flexibility and quick changes as needs are regularly shifting. Graph databases are the ideal solution for this kind of adaptive environment, where unified progress and management is becoming the standard.
Check out the fundamental differences between relational databases and non relational databases.
Use cases for graph databases
Did you know that all the top tech giants such as Facebook and Google are already utilizing the revolutionary power of graph databases to build products that generate mega revenues? All these companies use graph databases in a broad range of products, harnessing the force of big data connections to build irresistible products.
The use cases can be as vast as the imagination of any ambitious business. Here are the most interesting ones.
1. Recommendation engines
Real-time recommendation systems are very popular these days, especially in retail e-commerce, travel and social networks . The purpose of a recommendation engine is to predict customer preferences and recommend products or services they might be interested in whenever they are using your platform. The application of recommendation engines is quite exciting and is already being used by many companies to achieve immense success. But it would have been impossible to get this done were it not for graph databases.
With graph databases, you can easily discover connections and generate recommendations for users. These recommendations are based on powerful graph analytics that match preferences with relevant products or services.
2. Fraud detection
Graph databases are being used to detect fraud by tracking transactional relationships between entities over a period of time.
By uncovering patterns of behavior across multiple financial transactions, it is possible to detect anomalies that could indicate fraudulent activities.
3. Network Analysis
Graph databases can be used to trace connections between technology components such as wireless routers, cables and microwave links, providing administrators with useful insights such as the types of connections being used at any given time.
This is important because network administrators have an ever-increasing responsibility when it comes to managing digital networks. They must be able to identify problems quickly and ensure that operational performance is maintained. They can use graphs to quickly find out which paths data packets take through the network by understanding IP addresses and traffic flow over large portions of the infrastructure. This way, they can know exactly where problematic areas are during troubleshooting or when planning upgrades or changes in configuration.
4. Power grid scheduling
Utility power grids are some of the most complex infrastructures in existence, requiring a fine balance between efficient usage and stability. Traditional models used for managing these grids relied heavily on tables and relational databases, leading to slow response times when working with high levels of data. However, graph databases can revolutionize how utility grids are scheduled.
Graph databases store nodes and edges that represent real-world relationships, allowing more precise and detailed understanding of the power grid's dependencies. By organizing data into separate networks, utility providers can quickly make decisions regarding load shedding, rotational outages, or even unexpected problems like capacitor failures.
In addition to providing flexibility in scheduling decisions, graph databases can also detect patterns of consumer behavior which can then be used to anticipate upcoming demand changes in order to deliver superior service while reducing costs.
5. Chatbot systems
Graph databases provide a flexible and easily navigable structure that allows bots to quickly access data and respond to queries in real time. Large datasets can be retrieved at high speeds, be used to construct a useful inventory of customer preferences and generate meaningful dialogue based on the collected insights.
Questions are organized in natural language and converted into knowledge graphs which are stored in graph databases. A semantic parser is used to resolve the questions in a question-answer format. The answers are then retrieved from the knowledge graph and passed on to the user.
When should I use graph databases?
You should use graph databases when you need to model huge and highly interconnected data. So if you find yourself facing complex tasks such as connecting data from multiple sources or analyzing intricate relationships between people, groups, events, or concepts, then turn to graph databases.
They provide the flexibility to represent better inter-connectivity between elements and enable quick insights into connections between objects that are not easily modeled in other database systems.
Unlike traditional relational or document-based databases, graph databases provide powerful search capabilities and enable superior performance when performing complex queries on a large dataset. Additionally, graph databases make it much easier for developers to modify data structures without breaking existing applications or creating additional complexity due to changes in schema. Graph databases also boast greater scalability when dealing with high volumes of data whereas traditional systems typically suffer from poor responsiveness under such circumstances.
With these unique advantages, it's no surprise that graph databases are experiencing tremendous growth and becoming the go-to choice for many organizations. They offer tremendous potential for improving how we organize, access, analyze and utilize our ever-growing connected datasets.
Whether your organization handles large volumes of connected customer or product related data, graph databases could provide the crucial bridge you need to move from the isolated silos managed with traditional tools.
Should I use a graph database?
Yes, you should. Graph databases make it easy to explore relationships between data items, and are also very efficient at handling large quantities of data. They store data in a way that mirrors the structure of the real world, allowing them to quickly find the shortest path between two data points in the graph.
Why are graph databases better than relational databases?
Graph databases are better than relational databases because they are exceptionally good at handling complex data relationships. Relational databases are fundamentally a collection of tables, IDs and values. This approach is great for static data sets where relationships between the data are simple and well-defined. But it’s extremely difficult to use these databases for complex use cases. The more tables you have, the more relational databases will struggle as performance is degraded.
Graph databases, on the other hand, store data in graphs, which makes it easy to represent complex relationships between the data. This means that graph databases can easily handle large amounts of data more efficiently than relational databases. With this in mind, you should not have difficulties choosing between graph databases and relational databases.
Why graph database is a good choice?
A graph database is a good choice because it represents relationships between entities in a simple graph structure. In other words, it's well-suited for representing data with complex relationships.
A graph database can quickly traverse these relationships to find connections between points in the graph. This makes them ideal for complex applications such as fraud detection, network security, and recommendation engines.
Exploring Graph Databases?
Check out NebulaGraph Database and join the open source community to uncover more practical use cases of graph databases.