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Most data lakes are full of insights — but the most important relationships are invisible to SQL. Learn how to uncover hidden patterns in your S3 data lake using graph analytics — no ETL required. If you’ve ever felt like: Your SQL queries return rows, but not insight Important connections across datasets are hard (or impossible) to detect Your data lake isn’t actually helping your AI or ML workloads …this session is for you. 🔍 What you’ll learn: Why SQL struggles to reveal relationship-driven insights in data lakes How graph analytics exposes patterns traditional queries miss How to analyze S3 data without ETL or data duplication Real examples of finding fraud rings, entity connections, and hidden dependencies How relationship-aware data makes AI systems smarter and more explainable 🧠 Who this is for Data engineers and data architects Developers working with S3 data lakes Analytics and ML practitioners Anyone exploring databases for AI and modern data architectures 🚀 Why this matters: As AI adoption accelerates, relationship awareness is becoming just as important as raw data volume. Graph analytics helps bridge the gap between data lakes and AI-ready insights — especially when traditional SQL reaches its limits.This session shows how teams are moving beyond tables and joins to see their data as a connected system, not just isolated records. Code sample for this episode: https://github.com/awslabs/nx-neptune... Neptune docs: https://github.com/awslabs/nx-neptune... Learn more about Amazon Neptune: https://aws.amazon.com/neptune/ Previous episodes: 1. VectorDB vs GraphDB: https://www.youtube.com/live/qQeB2nuXNNo 2. Build your first GraphRAG Powered Agent! (No Graph Expert Required): • Build your first GraphRAG Powered Agent! (... 3. Build Knowledge Graph-powered AI Agents For Your Use Case: • Build Knowledge Graph-powered AI Agents Fo...