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The Future of Data: Connecting Knowledge with Graph Databases Data is rarely isolated; it’s woven into intricate webs of relationships. Graph databases embrace this complexity, replacing rigid rows and tables with nodes (entities) and relationships. They don’t just store data—they map it, making them the perfect solution for challenges where connections matter: think - recommendation engines, fraud detection, and, increasingly, knowledge graphs. Neo4j: The Graph Database Neo4j has rightfully claimed its spot as the leader in graph technology. Its intuitive Cypher query language, unmatched scalability, and seamless integration with AI make it indispensable for businesses wanting to leverage connected data. With Neo4j, navigating millions of nodes feels like a stroll in the park—albeit a park filled with complex data relationships. Highlights from the Final Neo4j Meetup of 2024 at IDEALondon Graph Visualisation and Diagramming: Sebastian Müller revealed how tools like yWorks Jupyter Notebooks can turn data into visual insights. With yWorks superior diagraming SDK, Graphs are less “graphs” and more “aha moments.” Check out their award winning represenation of Marvel Cinematic Universe - https://lnkd.in/ej-3pf9k https://lnkd.in/e5-Mj63m Autoscaling LLMs in Production: Yann Leger delivered an engaging talk on the intricacies of scaling AI-driven applications in production. He shared practical strategies for optimising resource usage and improving system performance, drawing on Koyeb's serverless architecture for handling AI workloads. Graph-Powered Open Ownership: Data, Challenges, and Queries Stephen Abbott Pugh from Open Ownership delivered an enlightening session on the Beneficial Ownership Data Standard (BODS), alongside Christophe Willemsen GraphAware. They delved into the intricacies of structuring and modelling ownership data, integrating identifiers like LEIs, and navigating the challenges of dataset ingestion The Power of GraphRAG Nyah M. stole the show with her engergy and her topic of Graph Retrieval Augmented Generation (GraphRAG). Imagine a knowledge base that doesn’t just retrieve facts but serves contextually relevant, interconnected insights to AI models. It’s Google meets Sherlock Holmes—smarter, faster, and always with the right connections. Neo4j is a game-changer. We are keen to embed solutions in our products and services in 2025. There are many possibilities and use cases starting with but not limited to : Knowledge Graphs: Fuel AI with context-rich, interconnected data for smarter outputs. Recommendation Engines: Go beyond “people also bought” to “here’s what you really need.” AI and NLP: Pair GraphRAG with AI to make generative models genuinely insightful- it’s not what you know but how it’s all connected. The graph revolution is here, and it’s anything but flat.
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