Real-Time Event Analytics

Fastest Event Broker

DASE Architecture Advantage

Powered by VAST’s stateless DASE architecture, every compute node has direct NVMe access to all-flash storage, eliminating broker partitioning, east-west replication, and coordination delays. This design delivers up to 2 million events per second per CNode—outperforming commercial event brokers by more than 20% while maintaining sub-millisecond latency at any scale.

Scaling Without Performance Degradation

The system scales linearly, sustaining 2 million events per second per CNode with consistent, sub-millisecond latency as nodes are added. Unlike Kafka deployments that experience coordination lag and latency spikes under load, VAST maintains predictable performance at any scale—critical for fraud detection, algorithmic trading, and AI inference where every millisecond counts

Topics-as-Tables

Instant SQL Querying on Live Streams

VAST streams Kafka topics directly into VAST DataBase tables, enabling analytics engines to query streaming data as standard SQL tables. This direct table access eliminates complex infrastructure management and accelerates time-to-insight for event-driven AI applications.

Simplified Management

Operational Simplicity: Kafka API, Zero Management

The VAST Event Broker is fully integrated and completely compliant with the Kafka API. Crucially, it implements the Kafka protocol without requiring traditional brokers, dedicated ZooKeepers, or manual partition management. This eliminates Kafka's most notorious operational burden, freeing your DevOps and SRE teams from the constant effort of tuning partitions, configuring replication, and babysitting ZooKeeper clusters. You get elastic event streaming performance with the familiarity of the Kafka API, all through a self-managing architecture.

Simplified Operations Through Architectural Unification

VAST fundamentally eliminates the most complex scaling burden: manual partitioning. Because the system's architecture does not rely on local storage limits or fixed shard ownership, performance and reliability are guaranteed without any complex partitioning strategy. By consolidating event streaming, analytics, and AI workloads into this single, unified system, you eliminate redundant data pipelines, separate orchestration layers, and the immense operational burden that comes with managing fragmented infrastructure.

Event Triggers

Event-Driven Automation

The VAST Event Broker generates real-time triggers the instant new data lands or changes. These triggers can automatically launch workflows or serverless Python functions running in the VAST DataEngine—automating any data pipeline, from transformation and enrichment to analytics or AI.

By eliminating manual scheduling and external orchestration, VAST turns pipelines into continuous, event-driven processes that operate directly on your data. An S3 event can trigger a function that analyzes an incoming object, joins it with historical context, and updates downstream systems—all within a unified platform. This enables real-time data processing, analytics, and AI workflows that react instantly to change.

Real-Time + Historical Analytics

Unified Real-Time + Historical Queries

With VAST, streaming events land directly into VAST DataBase tables (topics-to-tables), so fresh event data is immediately queryable alongside your historical datasets—no separate hot/cold stores, no sync jobs.

ACID writes and real-time upserts keep the latest state consistent, while the same SQL (and hybrid SQL+vector) queries can join live streams with years of history for operational analytics, RAG context, and alerting—all in one platform.

Flash-Native Storage Economics, Zero Tiering

VAST's all-flash DASE architecture leverages its game-changing Similarity-based data reduction to make storing all event data—from real-time streams to deep archives—economically viable on flash. This eliminates complex and costly data tiering, ensuring that every piece of historical data remains instantly accessible for analytics and compliance queries. Event-driven processing can drive incremental updates efficiently, ensuring new data is searchable within milliseconds, while the entire historical context is always online and ready for any analytical workload.