Agentic AI Meets Iceberg: The Future of Scalable Enterprise Data Intelligence.

The enterprise data landscape has undergone a dramatic evolution. A decade ago, data resided in rigid, separate systems: transactional databases for operations and data warehouses for business intelligence. This fragmentation led to inefficiencies, delays, and limited insights from critical data assets.

Today, Apache Iceberg is revolutionizing this space by unifying transactional and analytical data. Through Change Data Capture (CDC) and modern data engineering, companies are consolidating data into Iceberg tables, creating a permanent, versioned, and scalable historical record on object storage. Iceberg's capabilities in versioning, schema evolution, and metadata management ensure comprehensive data lineage and efficient storage at scale.

However, scale and storage efficiency alone do not translate to intelligence. Current AI-driven analytics approaches, such as RAG for unstructured documents or SQL queries for structured data, fall short. They lack the deep, contextual business intelligence needed to solve complex enterprise problems.

The true solution lies in integrating Agentic AI with Iceberg. Unlike basic search or query generation, Agentic AI deploys autonomous agents that can reason, hypothesize, explore data relationships, and adapt their analysis—much like a human analytics team. These agents leverage Iceberg's unified data, encompassing both current operational data and historical context, to uncover previously unattainable insights.

This session will delve into three crucial architectural considerations for implementing AI on Iceberg:

•Shift to On-Premises AI Deployment: The emergence of powerful, open-source AI models enables enterprises to deploy AI on-premises. This eliminates the need to move sensitive data to the cloud, significantly reduces infrastructure costs, and enhances data privacy and control.
•Building the "Business Understanding Brain": AI agents require a deep understanding of not only data structures but also the business logic, relationships, and domain knowledge embedded within Iceberg schemas. This necessitates intelligent schema interpretation and data sampling.
•Code Generation Over SQL Generation: For complex analytics, generating code (e.g., Python, Rust) offers greater flexibility and robustness than SQL generation. This enables sophisticated data processing, real-time reasoning, and integration capabilities that SQL alone cannot provide.
By combining Iceberg's unified data architecture with Agentic AI's reasoning capabilities and the flexibility of on-premises deployment, enterprises can transform their data lakes from passive archives into intelligent, evolving business partners. This marks the next evolution in data intelligence, delivering actionable insights at scale while maintaining enterprise security, privacy, and operational control.