Most data teams operate across multiple systems: warehouses, lakes, streaming layers, vector databases, and ML infrastructure. Each introduces its own pipelines, latency, and operational overhead. Data is constantly moved, transformed, and duplicated just to keep systems in sync. By the time data is ready, it is already stale.
This webinar series examines what changes when analytics and AI are built on a unified architecture instead of stitched-together systems.
Eight sessions. One complete picture.
Each session focuses on a core capability of the VAST AI OS. From database architecture and lakehouse design to real-time streaming, vector search, and end-to-end AI pipelines. Understand architecture decisions needed to evaluate, design, and build modern analytics and AI systems. Register today for all or individual sessions today!
Here is what we are covering in each session:
May 7th
Welcome to From Data to AI: VAST Analytics Explained series: Series kickoff. Why fragmented stacks break under AI workloads and how the VAST AI OS unifies analytics, streaming, and AI into a single architecture
May 21st
Beyond the Data Warehouse: Building AI-Ready Analytics with the VAST DataBase: HTAP architecture, sorted tables, and how VAST consolidates warehouses, databases, and streaming into one system with benchmark results at scale.
June 11th
Beyond the Lakehouse: Rebuilding ETL and Data Pipelines for AI on the VAST AI OS: Medallion architecture on VAST, native Iceberg support, and replacing complex ETL with continuous data processing for analytics and AI.
July 2nd
Beyond Kafka: Real-Time Event Architectures for AI with the VAST Event Broker: Kafka-API compatible streaming integrated directly into VAST. No separate clusters, no downstream pipelines, no data duplication.
July 23rd
Beyond Monitoring: AI-Driven Observability on VAST DataBase: As analytics and AI pipelines scale, observability becomes fragmented across multiple tools, making it difficult to understand system performance, data flow, and operational bottlenecks. Traditional monitoring approaches are not designed for real-time, AI-driven data systems.
August 13th
Beyond Data Preparation: Accelerating AI and Data Science on the VAST DataBase: Data science workflows are often constrained by data movement, disconnected tools, and limited access to large datasets. These limitations slow experimentation and make it difficult to work efficiently at scale.
September 3rd
Beyond Batch Pipelines: Building Real-Time AI Pipelines on the VAST AI OS: End-to-end AI from ingest to inference. DataEngine, InsightEngine, RAG, and automated document processing on a single platform.
September 24th
Beyond Vector Databases: AI-Scale Vector Search in the VAST DataBase: Hierarchical vector indexing native to VAST. Benchmark performance at billions of embeddings without a standalone vector database.