A data store for sparse connected data built for AI
High Availability
Nodes and Edges
Queries Per Second
Latency
Store any type of structured, semi-structured and unstructured data. From database tables to documents, images, audio and video, and raw binary data.
With native support for full-text search, Cypher, SQL and GQL you can query the data store in any possible way. And really, really fast.
Apply security labels to attributes, nodes and edges and combine them using logical operators to implement any imaginable scenario. Combine this with storage-level encryption and secure communications and you will have the most secure data store in the world.
Built on a shared-nothing distributed architecture to offer linear scalability, meaning that you can add more nodes or services to the cluster without affecting performance. Our native engine enables lightning-fast QPS and TPS with millisecond latency. With high concurrency, efficient traversals, and optimized memory usage, you can process sheer volumes of data blazingly fast.
With horizontal scalability and snapshots feature, it guarantees high availability even in case of failures. The snapshot feature allows users to take snapshots at any point in time so that they can use them to restore data at that specific time. If a disaster occurs, it can be recovered from snapshots without data loss. This process is fully automatic and transparent to clients.
Provides long-term memory to Large Language Models in the form semantic knowledge graphs.
One of the biggest obstacles to widespread artificial intelligence adoption is a lack of transparency as to how the AI system arrived at a particular decision. Using LLM memory, our platform can track how Generative AI perform Chain of Thought reasoning to reach a conclusion.
Enables marketing teams to connect different types of information to get a complete overview of how customers interact with their systems.
Money launderers typically create an intricate network of identities and accounts to funnel their ill-gotten gains. Deep Link Analytics helps identify fraud rings, suspicious transactions and unusual behavior.
Any network is a network of components and processes: the internet is an interconnected system of servers, routers, bridges, laptops, smartphones, and so on – and there are processes defining how these work together. Deep Liks Analytics helps identifying patterns of behavior associated with malicious attacks.
Assess and monitor Credit Risk and Regulatory Risk for customers and suppliers
From meter readings to information from network sensors, balancing a power grid requires consolidating signals from multiple levels of the power infrastructure and matching demand and supply with complex linear equations. Network operators can respond immediately to sudden spikes in demand or drops in supply, thus reducing operational risk and operating costs while improving reliability, efficiency, and customer experience.
Detect when a resource such as a storage array, server, network switch or router shows the signs of wear, requires maintenance or is nearing its peak capacity.
Supply and delivery pipelines have dozens, if not hundreds, of stages and an ability to analyze and understand the impact across many levels is essential. Our platform solution has advanced analysis and pattern recognition to identify product delays, shipment status, and other quality control and risk issues.
Data is symetrically distributed accross storage nodes based on optimal shards and partitions and Multi Group Raft architecture that take maximum advantage of NVMe SSDs
Storage nodes are separated from compute nodes to ensure maximum load distribution in a multi-node deployment configuration.
With a shared-nothing distributed architecture, NebulaGraph offers linear scalability, meaning that you can add more nodes or services to the cluster without affecting performance.
Support for both OpenCypher and ANSI SQL allows developers to integrate BigConnect into any application.
Full-text indexes are powered by Elasticsearch. This means you can use Elasticsearch the full-text query language to retrieve what you want.
A declarative graph query language designed for both developers and operations, that allows expressive and efficient graph patterns.
Based on Apache Spark, allows massively parallel graph computations and the full benefits of Spark's data processing, transformation and machine learning pipelines.
One-click backup and restore to local filesystem or S3-compatible cloud providers. Support for snapshots allows poin-in-time recovery.
An event-based data processing runtmie allows late processing of data, while preservind order of operations. Very useful for Machine Learning and AI data augmentation.
Low-dimensional, compact graph representations that store relational and structural data in a vector space, condensing complicated graph structures into dense vectors.
Query database metrics over HTTP to get a comprehensive overview of performance and availability of the entire data store.
Attribute, node and edge security labels can be applied and combined using expressions to achieve maximum flexibility. Supports also storage encryption and SSL comunication.
