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.
AnzoGraph DB is a massively parallel processing (MPP) native graph database built for 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.
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.
Zip through graph algorithms like Page Rank, Connected Components, Triangle Enumeration, Shortest Path, and more on your way to analytical truth.
AnzoGraph includes an RDFS+ inference engine that can create new relationships based on the vocabularies or ontologies in the existing data. Follows W3C standards.
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.
Extend your analytics with external data science algorithms like correlation, profiling, distributions and entropy analysis with more coming in every release.
Leverage the power of our MPP graph engine for your own algorithms. Use this crucial capability to customize your finished application.
Cambridge Semantics' AnzoGraph DB completed a load and query of one trillion (1012) triples running the Lehigh University Benchmark on the Google Cloud Platform in 1.98 hours versus the previous record of 220 hours, 100 times faster than any previous solution at the same data scale.Learn More
In this paper, we shine a light on the differences between graph OLTP and graph OLAP databases by comparing analytical style queries on Neo4j, a graph OLTP database, and AnzoGraph, a graph OLAP database. The results show that scaling for this type of query with huge volumes of data simply isn’t feasible on most graph OLTP platforms.Learn More
In this article for Medium, Angus Addlesee, Machine Learning Engineer at Wallscope, compares Linked Data Triplestores including AnzoGraph, Virtuoso, GraphDB, Blazegraph, Stardog and more head-to-head to see who’s fastest (spoiler: AnzoGraph is when you move into production).Learn More
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.
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.
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 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.
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.
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.
One of the original use cases for graph databases is for keeping track of social networks and understanding influence.
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.
Learn more about AnzoGraph, a native, Massively Parallel Processing (MPP) distributed Graph OLAP (GOLAP) database, providing hyperfast advanced analytics at big data scale.
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.
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.
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.
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.
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.