How to Find Hidden Data Relationships in Your S3 Data | Databases for AI

Описание: 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/

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