Predictive Maintenance for Factory Uptime with Neo4j Graph Analytics for Snowflake

In this technical walkthrough, we explore how Neo4j Graph Analytics for Snowflake can implement predictive maintenance strategies in manufacturing environments, avoiding costly machine failures and production downtime.

Learn how graph models outperform traditional relational databases when identifying:
– What machines are most likely to fail
– The critical nodes on production paths
– Alternative routing when a machine goes down

We cover core graph algorithms, including:
– Betweenness Centrality for finding hidden bottlenecks
– PageRank to identify key hubs
– Strongly Connected Components for detecting loops
– FastRP Embeddings + KNN for structural similarity analysis

This session features a full demo using Neo4j Graph Analytics for Snowflake.
You’ll see how to:
– Run graph algorithms using SQL, entirely inside Snowflake
– How to Prepare and Structure Data for Graph Analysis
– Scale analytics with serverless compute – no data movement needed

Whether you’re building a smart factory dashboard, exploring graph ML, or want a deeper look at real-world graph applications—this is for you.
Perfect for: Developers, Data scientists, ML engineers, backend devs, and architects working in analytics, manufacturing, or IIoT systems.

#GraphAnalytics #Neo4j #PredictiveMaintenance #Snowflake #MachineLearning #GDS #FactoryOps #DataScience #GraphAlgorithms

Explore Neo4j Graph Analytics for Snowflake → https://bit.ly/43rPYNK
Learn more about Neo4j AuraDB → https://bit.ly/451ssda
Access the code and dataset on GitHub → https://bit.ly/4kVQewj