Breaking the Kafka Ceiling: Streaming at 136M Messages per Second with VAST

As real-time workloads scale, traditional event streaming architectures increasingly become a bottleneck, driving up infrastructure cost, SSD consumption, and operational complexity. Apache Kafka and Kafka-compatible alternatives rely on shared-nothing designs, heavy replication, and consensus traffic that constrain throughput and make scaling expensive and unpredictable, especially under AI-era streaming and analytics demands.

In this VAST Live session, Jason Russler (Technical Director, Alliances, VAST Data) walks through how the VAST Event Broker fundamentally rethinks event streaming by eliminating the architectural constraints that limit Kafka at scale. Built on VAST’s Disaggregated and Shared Everything (DASE) architecture, Jason will demonstrate how the VAST Event Broker delivers linear scaling, dramatically higher throughput, and lower latency – achieving 136 million messages per second at 99% efficiency, while reducing write amplification and SSD requirements by orders of magnitude.

We’ll explore Jason’s insights on consolidating Kafka clusters onto VAST to simplify real-time pipelines, enable tabular access to streaming data for real-time and historical analytics, and lower total cost of ownership – all without sacrificing compatibility with existing Kafka producers and consumers. Using real-world use cases, including streaming ETL, fraud detection, and AI-driven pipelines, we will show how organizations can move faster with fewer systems and less infrastructure.

Key Takeaways:

• The Kafka Ceiling: Why legacy architectures hit performance, cost, and SSD scaling limits under modern workloads.

• The DASE Advantage: How the VAST Event Broker delivers breakthrough throughput and near-linear scaling.

• Operational Consolidation: The impact of Kafka consolidation on infrastructure footprint, SSD consumption, and TCO.

• Unified Analytics: How tabular access to streaming data simplifies pipelines and unifies real-time and historical analytics.

• Validated Use Cases: Real-world examples of high-speed streaming and AI pipelines in production.