Big Data Fabric 2.0 Drives Data Democratization
In this complimentary Forrester research report, Noel Yuhanna recommends that data-driven businesses make a big data fabric part of their data strategy to minimize time and effort spent ingesting, integrating, curating and securing data insights.
AnzoGraph: The Graph Database Engine for Enterprise Data on Demand
A perfect storm of business, regulatory, and technology factors are forcing organizations to gain control of their enterprise data assets. AnzoGraph provides the analytics engine to link and find those data assets at scale. Read this thought leadership piece to learn more.
Bloor Graph Database Market Update 2019
The Bloor Graph Database Market Update 2019 compares 16 graph database vendors, including Amazon Neptune, Microsoft Cosmos DB, Neo4j and AnzoGraph, and discusses the latest trends in the market and how the market has shifted over the last two years since their last Graph Database Market Update.
AnzoGraph Benchmark Report
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.
Robust, Agile, and Comprehensive: the Story of the Data Fabric
This whitepaper explains what an Enterprise Data Fabric is, how it allows organizations to model and integrate data at whatever level of granularity desired and how Anzo’s semantic technology adds a semantic layer of metadata that adds depth and meaning to the graph overall.
Get Smart: Why Semantics Are Key to Data Success
This vendor profile by the Bloor Group discusses how the ANZO platform’s combination of graph technology allow enterprises to analyze and visualize their data to understand both what is happening now as well as any number of possible future scenarios.
Accelerating the Path to Value with Business Intelligence and Analytics
TDWI’s research uncovered a number of important issues, including organizational issues, practices, and development methods, that can determine the timeliness and degree of “downstream” value delivered by BI and analytics.