Rethinking Data Pipelines: Building Continuous Analytics and AI Pipelines with VAST

Analytics pipelines ingest, transform, and prepare data for querying and analysis. AI pipelines extend that process to include enrichment, vectorization, and retrieval of context for AI applications. Both depend on coordinating multiple stages of data processing and execution. Whether they run in batch or continuously, the fundamental challenge is fragmentation between stages. 

Most enterprises rely on separate systems for ingestion, transformation, orchestration, storage, analytics, and vector retrieval. Each adds infrastructure to integrate, secure, and manage, and handoffs between systems introduce data movement, operational complexity, and latency. 

For AI applications, these delays have additional consequences. RAG and agentic systems depend on timely access to processed, indexed data to retrieve relevant context. When pipelines lag, applications operate on outdated information, even when newer data is already available. This white paper explains why that fragmentation compounds, and what a different architecture looks like.

What you'll learn

• Why fragmented pipeline architectures introduce data movement, latency, and operational complexity, regardless of whether processing runs in batch or continuously

• How VAST enables continuous analytics pipelines, using event-driven processing, immediately queryable streaming data, and SQL queries that combine fresh and historical datasets

• How VAST supports continuous AI pipelines, from ingestion and enrichment through model-based vectorization, retrieval, and RAG, with unified governance from raw data through vectors