NebulaGraph is an open-source distributed graph database with a shared-nothing architecture. It’s well suited for storing different types of data models and scales very well because of the separation of storage computation. NebulaGraph can handle extra-large volumes of data with zero – milliseconds of latency and can do quick graph analytics. It is widely used for recommendation systems, social media, security, knowledge graphs, AI, and capital flows.
In this post, we shall discuss the top benefits of NebulaGraph database. Let’s delve in!
Exceptional Performance
NebulaGraph database is not only lightning fast, but it can also handle complex and interconnected data sets with ease. Its cutting-edge algorithms and advanced search functions make it possible to quickly retrieve data from even the largest and most intricate networks. Unlike traditional relational databases, NebulaGraph database maintains its performance regardless of your data volume.
In fact, NebulaGraph is the only open-source graph database that can handle graphs with trillions of edges and vertices. So, it doesn’t matter whether you analyze social networks, track supply chains, or visualize complex systems; NebulaGraph makes it easy to explore and manipulate your data.
Support Simultaneous Queries
NebulaGraph database is a powerful tool that can update and query your data in real-time without sacrificing performance or speed. This database is optimized for rapid, targeted access to relevant data. This means you can retrieve the information you need in seconds without wasting time scanning through irrelevant data.
Data Integrity
NebulaGraph database is engineered to preserve the coherence of data relationships across a diverse range of data origins and configurations. When information is interconnected through a graph, organizations can guarantee the coherence and precision of their data throughout their systems.
Rather than keeping independent pieces of data in a database, NebulaGraph database unifies correlated data by connecting nodes and edges. This facilitates the visualization of interconnected data, allowing users to understand how changes to one piece of data will impact other areas, thereby augmenting data integrity.
Flexibility
Generally, a graph database like NebulaGraph provide the convenience of pliant online schema evolution and rapid query processing. Therefore, a user can continually incorporate or discard new vertex or edge types and their attributes to expand or reduce their data model.
This feature is particularly advantageous for managing dynamic and rapidly-evolving object types. Unlike relational databases, which struggle to adapt to such demands, NebulaGraph database seamlessly caters to these requirements, making it ideal for modern data management. Additionally, most of the pre-existing queries will continue to function effectively, enhancing their usability.
Improved Contextual Awareness
Since NebulaGraph database stores data as object relationships, it allows users to separate indirect nuances in interactions that might not be immediately apparent using conventional approaches like tabular relational models or hierarchical tree structures.
This heightened contextual awareness enables organizations to make better decisions by extracting valuable insights from their data. This is an invaluable advantage in today’s business landscape, where data often remains untapped. By harnessing the power of NebulaGraph database, organizations can effectively mine this data and gain transformative insights that drive organizational growth.
Supports AI Infrastructure
NebulaGraph database makes excellent infrastructure for AI—thanks to its well-organized relational information between entities, which enables the inference of indirect facts and knowledge. This feature is particularly appealing to machine learning experts, who appreciate the rich information and convenient data accessibility provided by graph databases, which other data models cannot easily match.
Scalability
The graph model schema and structure are incredibly flexible, enabling it to keep pace with ambitious data teams’ rapid data growth. Unlike traditional database technologies that require a time-consuming domain modeling process, graph databases can simply add to the current structure without affecting its current functionality.
Traditional database technologies often encounter scalability issues when integrating data from multiple sources. NebulaGraph database overcomes this challenge with its distributed nature, allowing for flexible scaling up or down as necessary. NebulaGraph provides linear scalability, allowing users to expand the cluster with additional nodes or services without compromising performance.
Improved Problem-Solving
NebulaGraph database addresses issues that are both impractical and feasible for relational queries, such as iterative algorithms like PageRank and gradient descent, as well as other data mining and machine learning algorithms. Research has demonstrated that certain graph query languages are Turing complete, which implies that any algorithm can be written on them.
While there are various query languages available, some have limited expressive capabilities. Therefore, it is important to pose numerous hypothetical questions to ensure NebulaGraph database can provide satisfactory answers before committing to it.
Bottom Line
Managing big data can be a real headache, especially when traditional relational databases fall short. But that’s not the case with native graph databases NebulaGraph. These databases offer tons of advantages that can’t be ignored. However, as with any new technology, there are a few bumps ahead. One major obstacle is the shortage of skilled developers compared to SQL experts.
On top of that, the graph database query language is not yet standardized. Also, there’s the marketing hype and incomplete offerings that have slowed down the adoption of this revolutionary technology. However, this should discourage you from adopting a NebulaGraph database, as its benefits are far better than its drawbacks.
