Graph Database - France

In this session we will guide you through an end-to-end graph data science workflow. This interactive session will provide hands-on experience and practical examples of how large language models can aid data scientists in performing complex graph analysis.

From initial data processing to final analysis, learn how these advanced models can streamline your workflow, enhance accuracy, and facilitate data-driven decisions. Suitable for data professionals of all levels, this workshop will give you the tools and knowledge to effectively integrate large language models into your data science toolkit.

In this hands-on workshop we will explore how graphs and Neo4j can add value when working with geospatial data and specifcally how Neo4j fits into geospatial data science workflows.
We will cover how to import and model geospatial data in Neo4j, the spatial functionality available in the Cypher query language, how to leverage Neo4j's Graph Data Science functionality when working with geospatial data, how to visualize the results of our analysis, and how to use Neo4j with common geospatial tooling in the Python ecosystem such as GeoPandas, Matplotlib, Shapely, and Fiona.

No initial setup is required. We will use the free Neo4j Sandbox cloud hosted Neo4j instances combined with Python notebooks that can be run either in GitHub's free Codespaces environment or Google's Colab cloud environment (or run locally in a local Jupyter environment).

In this episode we are looking at migrating Neo4j from v4 to v5 and how to deploy Neo4j on Kubernetes. We are looking at automated backups in your own data system by using Kubernetes Cronjob.

Guest: Jennifer Abel, Software Developer at synyx GmbH & Co. KG

Blog: https://synyx.de/blog/deploy-neodash-on-kubernetes/