This Week: Graph Data Science 2.0 and AuraDS Releases!

The big news this week for Neo4j was two major releases: Graph Data Science (GDS) 2.0 and AuraDS. GDS 2.0 brings a ton of new features, such as improvements to similarity algorithms, unified machine learning pipelines, and simplified graph projections using Cypher. Another big highlight in this release is a native Python client that allows you to establish a connection to your database and easily call GDS procedures without having to configure a driver or manage transaction functions. AuraDS is the new managed service for graph data science. Like AuraDB, it provides the ability to have a fully-hosted service to import, analyze, and visualize graph data. With AuraDS, data scientists can spend their time working with their data and won’t have to worry about managing infrastructure. Exciting times, to be sure! Read all about it in the blog!

Cheers, Clair Sullivan

FEATURED COMMUNITY MEMBER: Tomaz Bratanic

Tomaž Bratanič is a data analyst and engineer who turns everything into a graph. He is a prolific writer of blogs related to data science and the author of Graph Algorithms for Data Science from Manning Publications. A long time contributor to the Neo4j community, he has shared his expertise about graphs since 2017. Kudos to Tomaž! Follow him on Twitter.

BITE-SIZED NEO4J for DATA SCIENTISTS: Introducing GDS 2.0!

I demonstrate the new Python client for Graph Data Science 2.0 in this five-minute video using the latest Graph Data Science.

FREE TRAINING: Neo4j Data Scientist Courses

To celebrate the announcement of GDS 2.0 and AuraDS, we have added two new courses to #GraphAcademy in the Data Scientist learning path: Introduction to GDS and GDS Fundamentals.

INTERVIEW: Michael Simons on Neo4j OGM with Quarkus

Sebastian Daschner interviewed Michael Simons from Neo4j on all things Neo4j OGM with Quarkus. Michael recently published a Quarkus extension that everyone using Neo4j with Quarkus should check out. They chat about how the internals of Neo4j OGM and Quarkus work, best practices, history and comparison with Spring, and a look into the future.

ARTICLE: Building a Simple Recommendation Engine in Neo4j

Philipp Brunenberg describes how to implement a simple recommendation engine directly in Neo4j using only Cypher. The implementation is a handful of Cypher lines in a single query. He runs the query in real-time as the user interacts with the app, saving time and hassle on provisioning and maintenance.