Beyond the Warehouse and the Lakehouse

For years, enterprise analytics followed a familiar progression: data warehouses for high-performance analytics, data lakes for low-cost retention at scale, and lakehouses to reduce data movement between them. But, modern AI and operational analytics workloads increasingly combine continuous updates, low-latency retrieval, streaming ingest, and large-scale analytical queries against the same data. The typical response is a growing collection of warehouses, lakehouse tables, operational databases, vector stores, streaming systems, and ETL pipelines that must be continuously synchronized. . This white paper examines why neither the warehouse nor the lakehouse was designed for this converged workload model, and how VAST DataBase combines operational, analytical, streaming, and retrieval workloads on a single shared-everything architecture without compaction pipelines, dual-write patterns, or inter-system synchronization. What this paper covers

• Why warehouses and lakehouses are increasingly deployed together to support  to support modern analytics and AI workloads 

• How VAST’s Disaggregated Shared-Everything (DASE) architecture eliminates the coordination and synchronization overhead of shared-nothing systems

• Benchmark results: 210× higher update throughput vs. Apache Iceberg, 13× faster point lookups, and 30% faster TPC-DS analytical performance 

• How customers are consolidating multi-system architectures and how full consolidation reduces total cost of ownership by analytics stacks while reducing infrastructure cost and operational complexity by up to 60% Download the white paper today.