Beyond the Data Warehouse: Building AI-Ready Analytics with the VAST DataBase

Traditional data warehouses and lakehouse architectures rely on object storage and external metadata layers that introduce latency, serialization, and complex data pipelines. These trade-offs become limiting as AI workloads demand real-time ingest, high-concurrency transactions, and sub-second analytics at scale. In this webinar, we explored the architecture of the VAST DataBase, including its hybrid transactional and analytical design, sorted tables, and storage-layer optimizations. We will examine how these mechanisms enable high-performance analytics across large datasets and share benchmark results across analytic and transactional workloads, highlighting significant improvements in query performance at scale. Here’s what you'll learn from the session: - Why traditional warehouses and lakehouses introduce latency and complexity - How VAST streamlines  analytical, vector,  and streaming systems - How VAST DataBase delivers HTAP with row and column optimization - How sorted tables and pushdown improve performance at scale We demonstrated how the VAST DataBase consolidates operational databases, data warehouses, and streaming systems into a single platform, eliminating the need for separate systems and enabling a unified approach to 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.