Disclaimer
Please be advised that this product is currently inactive and unavailable for purchase. The details provided below serve as an overview of our proposed plans for this service, which may be subject to modification upon its availability for purchase on this website. You understand that the information provided is for reference purposes only and does not constitute or constitute any part of a sales offer.
Overview
CloudDB for Graph Database is a cloud-native service designed for managing extensive graph data efficiently. Drawing upon extensive practical expertise in handling large-scale graph datasets, it offers comprehensive solutions for storage, administration, real-time processing, querying, and visual exploration of massive graph datasets. Additionally, it facilitates rapid development by supporting property graph models and the TinkerPop Gremlin query language, streamlining the entire process of modeling, querying, and visually analyzing graph data.
Benefits
Storage of Massive Amounts of Graph Data
A Graph Database has the capacity to efficiently manage vast amounts of data, comprising tens of billions of vertices and trillions of edges within highly intricate networks. It seamlessly scales storage and computational resources in a linear fashion and offers comprehensive error tolerance and recovery functionalities.
Standard Query Language
Graph Databases adhere to the TinkerPop Gremlin standard query language and employ modular zero-code querying techniques.
Secure Data Environment
A Graph Database rigorously manages access to both its graph data and storage and computational resources. Only authorized service instances are permitted to manipulate the data.
Comprehensive Graph Data Services
Graph databases offer robust support for three primary graph functionalities: storage, computation, and visualization, facilitating efficient administration, exploration, and analysis of graph data.
Diverse Use Cases
Graph databases are versatile tools that cater to a spectrum of business applications, including financial transactions, security risk assessment, knowledge representation, advertising and recommendations, and device network mapping.
Ease of OPS
A Graph Database offers the capability to seamlessly scale both storage and computing resources while providing an array of operational features, including data backup and monitoring, thereby enabling linear scalability and comprehensive operational support.
Scenarios
Social Network
Financial Payment
Knowledge Graph
In social networking contexts characterized by close-knit circles and intricate relationships, Graph Databases excel in efficiently storing and querying such connections. Unlike traditional relational databases, which struggle with multi-level association queries like “Which of my friends enjoy singing?”, Graph Databases offer professional-grade solutions tailored to complex association analyses, ensuring efficient and effective data retrieval.
In the realm of financial transactions, the interconnections among entities like accounts, mobile numbers, and device IDs form an extensive network. Traditional relational databases face limitations in processing and analyzing these associations, hampering the detection of intricate transaction patterns and potential fraud. Leveraging advanced technologies like high-performance association queries and visual analysis, Graph Databases offer a professional solution for financial institutions and payment networks to efficiently tackle complex business challenges, providing comprehensive support for identifying and mitigating risks.
The knowledge graph stands out as a cutting-edge knowledge repository technology applicable across diverse industries, tightly integrated with graph databases, boasting extensive utility. Leveraging sophisticated extraction techniques on vast interconnected data, it facilitates the construction of expansive knowledge repositories. Graph databases serve as the foundational storage and querying framework for these knowledge bases, offering essential storage and retrieval functionalities.
Compute
Edge Computing
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Essential Storage Service
Data Migration
Data Process and Analysis
Relational Database
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Enterprise Distributed DBMS
Database SaaS Tool
Networking
CDN and Edge platform
Network Security
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Image Recognition
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AI Platform Service
Optical Character Recognition
Internet of Things
Message Queue
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Management and Audit Tools
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Education