Beyond the Data Lake: Benchmarking the VAST DataBase for Real-Time Lakehousing

Modern data architectures built on Apache Iceberg and object storage were designed for a different era. Batch analytics, high-latency lookups, and serial update workflows are no longer acceptable trade-offs as AI-era workloads demand real-time ingest, sub-second key lookups across billions of rows, and high-concurrency transactions. The result is bandwidth exhaustion, serial write bottlenecks, and sprawling multi-system pipelines that are expensive to operate and difficult to scale.

In this webinar, we benchmarked the VAST DataBase head-to-head against Apache Iceberg across analytic, transactional, and streaming workloads. We reviewed how VAST DataBase sorted table projections, native flash optimization, and storage-layer predicate push-downs deliver significantly faster point queries at scale, and how a real-world manufacturing customer achieved 18% faster analytics simply by moving from Iceberg to VAST.

We explored how the VAST DataBase consolidates operational data stores, transactional warehouses, and a streaming event architecture into a single system, replacing separate Kafka clusters, transformation engines, and vector stores with the VAST DataBase.

Key Takeaways:

• Why Iceberg and object storage architectures hit fundamental limits on lookups, updates, and real-time ingest

• How VAST DataBase sorted tables and storage-layer predicate push-downs deliver dramatically faster point queries at scale

• Benchmark results across analytic, transactional, and streaming workloads on real-world datasets

• How VAST DataBase consolidates operational, analytical, and streaming functions into a single, simplified system

• What transactional lakehousing looks like in practice, from ETL to streaming and AI pipelines