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 benchmark the VAST DataBase head-to-head against Apache Iceberg across analytic, transactional, and streaming workloads. We'll show 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'll also show 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
Choose your preferred time slot and join us for this exclusive webinar. We’re excited to have you participate!