Here I show how Kafka can push millions of update messages to a Neo4j graph. Super cool and super fast. Some setup work, not too hard. »
The Power of Graph Analytics
Data is great, but true insight comes from examining relationships between data points.
Hiding somewhere in your data are patterns of relationships representing money on left on the table - if only they could be discovered.
Some of the largest and most successful companies, including Facebook, eBay, LinkedIn, PitneyBowes, Cisco and Microsoft analyze their most important data in graph databases.
These companies use graphs to efficiently compute recommendations, organize social networks, manage master data, optimize marketing and detect fraud - complex pattern analyses simply not possible in SQL databases.
This ability to quickly extract patterns from massive amounts of data is why graph databases are at the forefront of enterprise analytics.
There are a number of graph databases, but the top ranked and most well-suited for enterprise deployment is Neo4j.
Neo4j is a highly scalable, native graph database that leverages data relationships as first-class entities
Meet Neo4j - The World's Fastest Graph Database Platform
- NoSQL, native graph database, open-source & ACID compliant
- Stores nodes & relationships between nodes
- Sparse, schema-less, storing only what's needed (no NULLS)
- Blazingly fast, outperforming SQL RDBMS by orders of magnitude
- Infinitely scalable, limited only by hardware
- Cypher (Neo4j's declarative graph query language) is sophisticated, yet easy to learn
- Enterprise-ready with SSL APIs, role-based security and high availability
- Has the largest, most active developer community of any graph database
This is Part 1 of two-post series on how to use graphs and graph analytics to make make better marketing recommendations, starting with marketing attribution modeling. »
In Part 2 of this series, I'll show you how to easily compute event-based similarities in a Neo4j graph and construct a simple marketing recommendation engine. »
Here I take a look at how indexes and batching efficiently breaks out parent nodes from millions of child nodes in a very large Neo4j graph. »
In this post, I'll share tips for importing massive files into a Neo4j graph and also take a look at the impact of Bitlocker on Azure. »