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Description
AnzoGraph DB boasts an extensive array of analytical features that can significantly improve your analytical framework. Check out this video to discover how AnzoGraph DB operates as a Massively Parallel Processing (MPP) native graph database specifically designed for data harmonization and analytics. This horizontally scalable graph database is optimized for online analytics and tackling data harmonization issues. Addressing challenges related to linked data, AnzoGraph DB stands out as a leading analytical graph database in the market. It offers robust online performance suitable for enterprise-scale graph applications, ensuring efficiency and speed. AnzoGraph DB employs familiar SPARQL*/OWL for semantic graphs, while also accommodating Labeled Property Graphs (LPGs). Its vast array of analytical, machine learning, and data science tools empowers users to uncover new insights at remarkable speed and scale. By prioritizing context and relationships among data, you can enhance your analysis significantly. Additionally, the database enables ultra-fast data loading and execution of analytical queries, making it an invaluable asset for any data-driven organization.
Description
The initial step in your process should always be modeling your data, as applications may come and go, but data remains constant. After successfully implementing your model, your CubicWeb application will operate, allowing you to gradually introduce valuable features for your users. RQL, which is based on your application model, is a concise language that emphasizes the attributes and connections inherent in the data. While it shares similarities with SPARQL, RQL is generally more user-friendly. Once a RQL query retrieves a data graph, various views can be applied to present the information in the most pertinent format. This design principle is fundamental to the entire CubicWeb architecture. Permissions are intricately defined within the data model, allowing for exceptional precision. Furthermore, any RQL query made to the engine automatically undergoes security checks to ensure safe handling. CubicWeb utilizes a conventional SQL database for data storage and management, with PostgreSQL being the favored choice among its users. By leveraging these capabilities, CubicWeb not only enhances functionality but also prioritizes security and data integrity.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Apache Kafka
Docker
Google Cloud Platform
Jupyter Notebook
Microsoft Azure
Python
Tableau
Integrations
Amazon Web Services (AWS)
Apache Kafka
Docker
Google Cloud Platform
Jupyter Notebook
Microsoft Azure
Python
Tableau
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Cambridge Semantics
Founded
2007
Country
United States
Website
www.cambridgesemantics.com/anzograph/
Vendor Details
Company Name
CubicWeb
Website
www.cubicweb.org