Analytics on Connected Data

Connected data has become a central element of
analytics projects and solutions.

A knowledge graph is a natural data model with the capability to harmonize and integrate data as well as apply algorithms and analytic functions needed for connected data analytics.

As an enterprise-scale knowledge graph platform, Anzo allows users to rapidly build scalable knowledge graphs to connect data from multiple sources – iteratively remodeling the data as required for new analytics use cases. Anzo’s in-memory graph engine, AnzoGraph, provides entire libraries of graph algorithms, data science functions and traditional OLAP analytics functions that can be combined in the same query to tackle any analytics problem, at scale, within the knowledge graph. Anzo’s query generation capability allows non-technical users to express complex analytic questions without writing queries or code.

APPLY WAREHOUSE ANALYTICS AND GRAPH ALGORITHMS TOGETHER, AT SCALE

Apply advanced analytics to highly blended datasets in a knowledge graph to find previously hidden insights. Built-in libraries of hundreds of data science primitives and analytic functions/formulas support traditional OLAP and graph specific analytic functions as well as data science, ML/AI, and geospatial functions.

DEEP INTEGRATION WITH DATA SCIENCE PLATFORMS

Leverage and exploit investments in data science platforms and algorithms by applying them to highly optimized blended datasets in a knowledge graph. Built-in deep integration with data science platforms through the Apache Arrow Flight Protocol, HTTP/REST APIs, and user-defined-extensions (UDX).

Built-In Analytic Libraries

 

 

 

 

Inferencing and Semantics Analytics – AnzoGraph’s descriptive vocabulary (SPARQL*/OWL) makes relationships first-class citizens of the database.

Build Your Own – We’ve got you covered with the tools to code your own custom functions that use the power of the MPP cluster.

Graph Algorithms – Graph algorithms like clustering, centrality, pathfinding, community detection, and graph similarity offer a new level of insight.

Data Warehouse-style Analytics – Perform deep analytics as you would on your current data warehouse or augment your architecture for new workloads

Data Science Algorithms – You can apply data science algorithms like correlation, profiling, distributions and entropy analysis.

Geospatial – Complex location relationship analytics are part of our offering. OGC and GeoSPARQL standards are supported.

 

Anzo's built-in libraries let you analyze connected data at scale.

Best Practices for Implementing Data Science Sandboxes

Build a data sandbox that supports data modeling, integration, and scale.

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The Data Fabric for Machine Learning

How advances in semantics can help us be better at Machine Learning

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Applying Graph Algorithms on Knowledge Graph

Finding Influencers in a Real-World Social Network

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Explore Anzo's Full library of Analytic Functions

Visit Anzo's documentation library to explore a full list of built-in analytic functions.

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