This Week in Neo4j: Py2Neo, Time Series, Data Clusters, Graph Database Internals and more

Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
This week, we bid farewell to Py2Neo, visualise DateTime Data, explore a new Algorithm for Data Clusters and have an overview of Neo4j and Graph Databases.

Remember our GenAI Stack together with Docker, LangChain and Ollama? The announcement video from DockerCon is now available to watch. In our GraphAcademy, we also have two courses on Neo4j & LLMs available: Fundamentals and How to build a Neo4j-backed Chatbot.

I hope you enjoy this issue,
Alexander Erdl


Chris Zirkel is Co-Founder of, a young software startup dedicated to developing a new graph-powered Data Collaboration Platform to change how companies work with their data.
Connect with him on LinkedIn.

In his session at NODES “Streamline Your Development With GitHub Actions: Build, Test, and Deploy Custom Code” Chris shows you how to create a GitHub Actions workflow that runs on every commit and pushes to your GitHub repository, enabling you to automate the build, testing, and deployment of your latest changes

Chris Zirkel

Py2Neo: Py2neo Is End-of-Life — A Basic Migration Guide

After a long and excellent ride, the much-loved py2neo, which bridged graph thinking with Pythonic principles, has come to an end by the decision of its creator and maintainer, Nigel Small. The py2neo GitHub project has moved to neo4j-contrib/py2neo but is no longer maintained. Marius Conjeaud guides you through migrating your data from py2neo to the official driver.
TIME SERIES DATA: Astronomical insights with Neo4j and KronoGraph
Corey Lanum provides a hands-on approach to working with DateTime data with Neo4j. To demonstrate, he uses the astronaut database from Supercluster. It lists every human who has ever been to space, starting with Yuri Gagarin in 1961 and ending with the astronauts in space right now.
DATA CLUSTERS: Clustering Graph Data With K-Medoids
Nathan Smith explains how to work with the K-medoids algorithm to discover data clusters. It assigns nodes to a cluster based on the shortest path to a medoid. The algorithm optimises the selection of medoids to create the shortest total distance from nodes in the graph to their nearest medoid.
VIDEO: Graph Database Internals: Neo4j with Michael Hunger

Kaivayla Apte from The Geek Narrator talks to Michael Hunger about Neo4j. Starting with a general intro on Data Modelling and Cypher, they move on to the Neo4j Storage Layer, Graph API and Partitioning Strategy.

TWEET OF THE WEEK: 5.15 Technologies

Don’t forget to share it if you like it!