This week Will and I interviewed Ward Cunningham as part of the Neo4j online meetup and we launched the first version of the much awaited Kafka Connector. Neo4j 3.5 was also released and Jennifer kicked off an exciting series of posts on the Marvel Universe.

1. This Week in Neo4j
1st December 2018

2. Mark Needham
Developer Relations Engineer at Neo4j
mark.needham@neo4j.com / devrel@neo4j.com
@markhneedham
2

3. The week's graph database news
in one handy slide deck!
Find the archive at
neo4j.com/tag/twin4j

4. Featured community member: Tom Michiels
Learn more about Tom

5. In last week's online meetup, Ward Cunningham showed
off El Dorado - a tool he's built to collect metadata from all
aspects of NewRelic's operations to build a graph based
structure warehouse.
Iterative modeling of corporate resources in a
rapidly growing company
Watch the video

6. Jennifer Reif has started a series of blog posts in which
she’ll show how to build a full stack application with Spring
and Neo4j based on the Marvel dataset. In the first post
Jennifer evaluates Marvel’s API, and
then comes up with an initial graph
data model after several
whiteboard sessions.
Create a Data Marvel : The Data Model
Read the blog post

7. This week we released the first version of an integration
with Apache Kafka. The Kafka Producer communicates data
from Neo4j’s event listener to a Kafka topic, and on the
Consumer side you can specify a piece of Cypher that will
execute every time a message is received on a
Kafka topic.
Read the blog post
New Neo4j Integration with Kafka

8. Neo4j 3.5 was released this week, and Michael has written
a quick post on the community site explaining some of the
features that will make your lives easier. This release
contains support for full text search, indexed backed order
by queries, Off-heap transaction state, compiled
expressions, and more.
Neo4j 3.5 Released
Read the blog post

9. Tomaz Bratanic has written a blog post showing how to use
Neo4j’s similarity algorithms as part of a k-Nearest
Neighbours classification algorithm to work out how
members of congress are likely to vote. Tomaz explains
how to use the similarity algorithms to find each node’s top
k neighbours, and then select the majority class
from those neighbours as the predicted class.
kNN: Classification of members of congress using
similarity algorithms
Read the blog post

10. If you liked this check
out the blog post
This Week in Neo4j - 1st December 2018