Beyond the Lakehouse: Rebuilding ETL and Data Pipelines for AI on the VAST AI OS

Lakehouse architectures were designed to simplify analytics, but still rely on complex ETL pipelines, object storage constraints, and disconnected compute engines. As AI workloads require continuous data processing and real-time pipelines, these architectures introduce unnecessary data movement and operational overhead.

In this session, we examine how the VAST AI OS enables a modern lakehouse architecture with native support for object storage, open table formats, and integrated compute. We will show how organizations can establish and manage a medallion architecture directly on the platform, with governed, business-ready data products.

Here’s what you'll learn from the session:

• Challenges of traditional lakehouse architectures 

• How VAST simplifies medallion architecture 

• Native support for Iceberg and open table formats

• How ETL pipelines are replaced with continuous data processing

We will also demonstrate how ETL pipelines can be simplified and automated within VAST, reducing pipeline complexity and enabling continuous data processing for analytics and AI.

Looking for more? This session is part of From Data to AI: VAST Analytics Explained, a free 8-part technical series on building modern analytics and AI systems on VAST AI OS. Register for more sessions here.