AnzoGraph® DB

Build Your Solutions on a Fast, Scalable Database

Take on new analytical challenges with AnzoGraph DB, a market leading graph analytics database

Like all databases, graph databases store facts, but they also keep track of how those facts are connected.

Leverage the power of AnzoGraph DB analytics to not only perform BI-style analytics, but to go further with knowledge graphs, graph algorithms, inferencing and more. AnzoGraph DB delivers on this broadened set of analytical capability, delivered at unparalleled speed and scale.


What is AnzoGraph DB

AnzoGraph DB is a massively parallel processing (MPP) native graph database built for diverse data harmonization and analytics at scale (trillions of triples & more), speed and deep link insights. Use it for embedded analytics that require graph algorithms, graph views, named queries, aggregates, built-in data science functions, data warehouse-style BI and reporting functions.

Analytical Capabilties

SPARQL and CYPHER

AnzoGraph DB is the only graph database that supports both RDF/SPARQL standards and OpenCypher (2019 release). Use the language you know to get the results you need.

Graph Algorithms

Zip through graph algorithms like Page Rank, Connected Components, Triangle Enumeration, Shortest Path, and more on your way to analytical truth.

Inferencing

AnzoGraph includes an RDFS+ inference engine that can create new relationships based on the vocabularies or ontologies in the existing data. Follows W3C standards.

Labelled Property Graph

Our engine supports labelled properties under the new proposed W3C standard. You can also use OpenCypher soon. Apply properties to vertices and edges for extra analytical firepower.

Data Science and Statistical

Extend your analytics with external data science algorithms like correlation, profiling, distributions and entropy analysis with more coming in every release.

Define Your Own

Leverage the power of our MPP graph engine for your own algorithms. Use this crucial capability to customize your finished application.

Scale and Benchmarks

Trillion Triple Benchmark

Cambridge Semantics' AnzoGraph DB completed a load and query of one trillion (1012) triples 100 times faster than any previous solution at the same data scale.

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TPC-H Benchmark

See the differences between graph transactional and analytical databases by comparing analytical style queries on a popular OLTP database vs AnzoGraph DB.

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Comparing Triplestores

In this article, Angus Addlesee, an engineer at Wallscope, compares Linked Data Triplestores including AnzoGraph DB, Virtuoso, GraphDB, Blazegraph, Stardog and more head-to-head.

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Use Cases and Applications

Knowledge Graph

Organizations are using graph databases to build Knowledge Graphs to provide common business understanding to the data harmonized from diverse sources. Knowledge Graphs stores entities and relationships in data and allows users to search, analyze and use this connected data to accelerate vital new discoveries.

Unstructured Data Analytics

Combined with Natural Language Processing (NLP), graph database offers a free-form repository to store the output of NLP, which is often formatted in RDF triples and use of such data for data discovery and analytics.

Key Influencer Analytics

Analyze all customer data to find key opinion leaders. Gain new insight into each customer’s likes and dislikes in relation to other customers with similar location, similar demographics, etc. Discover new correlations between customers with inferencing, for more personalized and engaging customer experiences.

Recommendation Engine Analytics

Recommendation engines are perfect in a graph database when you want to make use of algorithms and data to recommend the most relevant items to a particular user.

Fraud Analytics

Use Graph to help detect fraudulent trading patterns and transactions in real-time. Semantically identify and understand the intricate relationships between entities and transactions, including the many individuals and organizations involved with those transactions.

Path Optimization Analytics

Analyzing how things (objects) connect and interact with each other can be very powerful. Graph databases are uniquely qualified to help with this relationship analytics.

Social Analytics

One of the original use cases for graph databases is for keeping track of social networks and understanding influence.

AI & Machine Learning

We think that the emerging world of AI and machine learning offer workloads that are well-suited for graph databases. Many of the machine-based algorithms are graph algorithms such as community detection algorithms, pathfinding algorithms, similarity or centrality algorithms.

Deploy



Resources

AnzoGraph Datasheet

Learn more about AnzoGraph, a native, Massively Parallel Processing (MPP) distributed Graph OLAP (GOLAP) database, providing hyperfast advanced analytics at big data scale.

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Bloor Graph Database Market Update 2019

This report compares 16 graph database vendors, including Amazon Neptune, Microsoft Cosmos DB, Neo4j and our own AnzoGraph in terms of analytics, ease of use, features, performance, scalability and more.

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Understanding Graph Databases

In this blogpost we give a very high-level explanation of graph databases - what they are and how they provide meaningful insights into the relationships between your data.

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If You’re Not Using Graph OLAP, Here’s Why You Should

This blogpost discusses the difference between Graph OLAP and OLTP databases and the analyses that can be run using OLAP databases that can't be run using OLTP databases.

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On-demand Webinar: Scalable, Fast Analytics with Graph - Why and How

Watch this on-demand webinar as they demonstrate how AnzoGraph DB can be used to do difficult-to-perform analytics on large data sets and to explore and uncover new opportunities using the Graphileon user interface.

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On-demand Webinar: Sustainability Investment Research Using Cognitive Analytics

Watch this on-demand webinar to explore how portfolio managers are using the Parabole/ AnzoGraph DB integration for conducting ML and cognitive analytics at scale to identify potential risks and new opportunities.

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