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.
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.
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.
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.
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.
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.
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.
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.