Welcome to the I can’t believe it’s already February edition of This Week in Neo4j.
This week we have five (FIVE!) releases of different projects in the ecosystem, including the Neo4j Desktop, Graph Algorithms Library, and a brand new Python library that’s great for ETL jobs.
Elsewhere there’s a a master class in writing a stored procedure with Max De Marzi, Jennifer Reif and Mark Heckler show us how to build an application of the Marvel Universe using Neo4j and Spring Boot, and Andrea Santurbano shows us how to build a just-in-time data warehouse with Neo4j Streams.
3. The week's graph database news
in one handy slide deck!
Find the archive at
neo4j.com/tag/twin4j
4. Featured Community Members: Kunal Yadav,
Paramveer Singh, Raghu Madhava
Learn more about Kunal, Paramveer, and Raghu
5. In this week’s Neo4j Online meetup, Jennifer Reif and Mark
Heckler showed us how to build a graph backed application
using Spring Data Neo4j, Spring Boot, and data sourced
from the Marvel API.
Getting Graphic: Extract maximum value from your
data using Neo4j and Spring Data
Watch the video
6. Oskar Hane released version 1.1.14 of the Neo4j Desktop, a
release which had a focus on tightening up security for our
users and evolving integration support for third party app
vendors.
Neo4j Desktop Release: Command Bar, Deep Linking,
Improved Security
Read the release post
7. This week we released a new version of the Graph
Algorithms library. This release introduces the ArticleRank
and Pearson Similarity algorithms, Cypher projection
support for similarity algorithms, and
performance optimizations for the
Louvain algorithm.
Read the release post
Graph Algorithms Release: Pearson Similarity,
ArticleRank, Louvain Performance Improvements
8. Max De Marzi has written a blog post containing slide
decks, source code, and performance test source code, to
get you up and running with writing Neo4j
stored procedures.
Learn you a Neo4j stored procedure
Read the blog post
9. Andrea Santurbano has written a blog post showing how to
to create a Just-In-Time Data Warehouse using the newly
released Neo4j Streams module with Apache Spark’s
Structured Streaming APIS and Apache Kafka.
How to leverage Neo4j Streams and build a
just-in-time data warehouse
Read the blog post

10. If you liked this check
out the blog post
This Week in Neo4j - 2nd February 2019