Graph Data Platforms: Collections vs. Connections
By David Penick
January 11, 2021 < 1 min read
Read this blog to find out how graph databases differ from SQL and NoSQL databases, empowering you to reap the benefits of connected data.
Explore: AI / Machine Learning • Collections vs. Connections • Data Relationships • graph data platforms • nosql • real-time recommendation • relational databases vs. graph
Neo4j + AWS Lambda & API Gateway to Create a Recommendation Engine
By Bryce Merkl Sasaki
November 8, 2016 8 mins read
The Challenge of Findability and Why Graphs? Here at the SAVO Group, we are in the business of sales enablement via Software as a Service (SaaS). One of the tools that we provide our customers is the ability to prescribe… Read more →
Explore: amazon web services • API Gateway • AWS • cypher • EC2 • Lambda • Microsoft SQL Server • python • real-time recommendation • Recommendation Engine
Building a Real-Time Recommendation Engine with Data Science
By Bryce Merkl Sasaki
August 17, 2016 12 mins read
Editor’s Note: This presentation was given by Nicole White at GraphConnect Europe in April 2016. Here’s a quick review of what she covered: Basic graph-powered recommendations Social recommendations Similarity recommendations Cluster recommendations – What we’re going to be talking about… Read more →
Explore: cartesian product • clustering algorithm • cypher • data science • Euclidean distance • graphconnect • r • real-time recommendation • Recommendation Engine • social recommendation
(Un)common Use Cases for Graph Databases
By Bryce Merkl Sasaki
April 18, 2016 4 mins read
At GraphAware, we live and breathe Neo4j. For three years, we have been helping customers around the world embrace this amazing technology as a solution to many interesting problems. Mainstream applications of graphs, such as real-time recommendations, fraud detection, impact… Read more →
Explore: cypher • fraud detection • graph database • GraphAware • graphconnect • investment portfolio • law enforcement • neo4j • real-time recommendation • rules engine
How Walmart Uses Neo4j for Retail Competitive Advantage
By Bryce Merkl Sasaki
December 14, 2015 3 mins read
In a little over 50 years, Walmart grew from a small, family-owned business to become the world’s largest public corporation, with over 2 million employees and annual revenues of $470 billion. But Walmart didn’t just become the world’s largest retailer… Read more →
Explore: competitive advantage • ecommerce • graph database • neo4j • product recommendations • real-time recommendation • Recommendation Engine • retail competitive advantage • sustainable competitive advantage • Walmart