VAST Native Query Engine

In-Place Analytics

Run analytics directly where data lives. The VAST Native Query Engine executes aggregations and statistical functions in-place, eliminating data movement and external pipelines. This simplifies architecture and delivers faster insights on live data.

Execute SQL-Based Aggregation Directly on Live Data

The VAST Native Query Engine brings SQL-based analytical execution directly into the data platform, enabling aggregation and statistical queries to run on live, continuously ingested data. Core functions such as SUM, AVG, COUNT, variance, and correlation are executed in place, without exporting data to external systems or relying on precomputed datasets. This approach eliminates the latency and operational overhead associated with batch pipelines and data synchronization. By keeping analytical execution aligned with the data layer, organizations can query fresh data in real time while maintaining consistency, reducing complexity, and avoiding the need for separate warehouse infrastructure.

SQL Analytics Without External Processing Engines

Beyond standard aggregation, the engine supports a range of statistical and analytical functions directly within SQL queries. Capabilities such as regression (regr_slope, regr_intercept), quantile estimation, and percentile calculations enable deeper analysis without requiring Spark, Trino, or specialized analytical systems. These functions operate directly on operational data, allowing users to explore trends, relationships, and distributions without introducing additional processing stages. By embedding statistical analysis within the core execution framework, VAST enables more advanced analytical workflows while maintaining a simplified architecture and avoiding the need to move or transform data across systems.

Eliminate Data Movement and Pipeline Dependencies

Traditional data architectures depend on pipelines to move, transform, and prepare data for analysis. The VAST Native Query Engine removes this requirement by executing analytical logic directly on in-place data. Queries operate on the same data used for ingestion and storage, eliminating the need for ETL processes, intermediate datasets, or synchronization across systems. This reduces latency between data arrival and analysis, simplifies operational workflows, and minimizes the risk of inconsistency. By removing pipeline dependencies, organizations can move from batch-oriented processing to real-time analytics without increasing infrastructure complexity.

Unified Engine for Vector and SQL

Combine vector search, SQL filtering, and analytics in a single execution path. The VAST Native Query Engine processes similarity search, structured data, and aggregations together, eliminating cross-system joins and accelerating hybrid AI and analytics workflows.

Run Vector Similarity Search as a Native Data Platform Capability

Vector similarity search is executed natively within the VAST Data Platform, using the same execution framework as all other queries. Vectors and their associated metadata are stored together, allowing similarity scoring, filtering, and access control to be applied within a single execution path. This eliminates the need for external vector databases or specialized retrieval systems. By embedding vector search directly into the platform, VAST ensures consistent performance, governance, and scalability, while enabling AI-driven workloads to operate on the same data and infrastructure as analytical queries.

Execute SQL Filtering and Aggregation Within the Same Engine

Structured queries, including filtering, projection, and aggregation, are executed within the same engine that powers vector retrieval. This allows SQL-based operations to be applied directly to operational data without relying on external query engines or warehouse systems. By consolidating structured query execution into the core platform, VAST reduces architectural complexity and ensures consistent behavior across workloads. Analytical queries can be expressed and executed without additional processing layers, enabling faster access to insights while maintaining alignment with the platform’s governance and security model. Combine Vector Search with SQL for Hybrid Search

The VAST Native Query Engine will enable hybrid search by combining vector similarity search with SQL-based filtering and aggregation in a single query flow. Results from similarity search can be immediately refined using structured predicates, correlated with metadata, or aggregated to produce analytical outputs—all without exporting data to external systems. This unified execution path allows users to connect embeddings with business context in real time, supporting use cases such as AI-assisted investigation, semantic analytics, and contextual search. By eliminating the need to stitch together results across systems, VAST simplifies query logic while improving performance and consistency.

Simplified Data and Execution Architecture

Eliminate external engines and complex pipelines. The VAST Native Query Engine runs analytics directly on data, reducing system sprawl and operational overhead while improving performance and reliability at scale. Eliminate Data Duplication and Reprocessing Across Systems

Because analytical execution occurs directly on in-place data, there is no need to duplicate data into separate analytical systems or maintain multiple copies for different workloads. This reduces storage overhead and eliminates the need for reprocessing pipelines to keep datasets synchronized. By operating on a single source of truth, VAST improves data consistency and reduces the risk of divergence between systems. This approach also simplifies data management, as organizations no longer need to track and maintain multiple versions of the same dataset across different platforms.

Scale Without Partitioning, Sharding, or Data Fragmentation

Built on VAST’s Disaggregated Shared-Everything (DASE) architecture, the platform allows every compute node to access all data directly, without requiring partitioning or sharding. Analytical workloads can scale by adding compute resources without redistributing or reorganizing data. This eliminates hotspots, reduces coordination overhead, and ensures consistent performance across the dataset. By avoiding data fragmentation, VAST simplifies scaling while maintaining efficient query execution, even as data volumes and workload demands increase .

Streamline External Query Engines

The VAST Native Query Engine eliminates the dependency on external analytical engines such as Spark or Trino for core aggregation and statistical processing. By executing analytical logic directly within the platform, VAST reduces the number of systems required to support data workflows. This consolidation simplifies deployment, reduces operational overhead, and minimizes the complexity associated with managing multiple distributed systems. Organizations can perform analytical queries without provisioning, tuning, or maintaining separate processing engines, resulting in a more streamlined and efficient architecture.

Operate on a Single, Consistent, and Governed Data Platform

By consolidating storage, vector processing, and analytical execution into a single platform, VAST provides a consistent governance and access model across all workloads. Security, access control, and auditing are applied uniformly, regardless of whether queries involve vector search or SQL-based analytics. This reduces the complexity of managing policies across multiple systems and ensures that data remains governed throughout its lifecycle. Operating on a unified platform simplifies compliance, improves visibility, and enables organizations to maintain control over their data without introducing additional layers of infrastructure.