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 explore 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.

We will also show 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.

Key Takeaways

  • 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

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