AI Lakehouse Architecture

Build an AI Lakehouse on VAST

Build an AI lakehouse while preserving the open analytics ecosystem your teams already know. VAST simplifies the architecture beneath Spark, Trino, SQL, and Python, enabling continuously updated analytics with dramatically less infrastructure letting you design an AI Lakehouse for today’s workloads.

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AI Lakehouse
The AI Lakehouse Challenge

The Lakehouse Solved Yesterday’s Problem

The lakehouse simplified analytics by reducing data movement and embracing open ecosystems. But modern lakehouses increasingly support AI alongside analytics, requiring continuous updates, operational data, streaming, and vector retrieval that drive additional infrastructure complexity.

Immutable Files

Open table formats simplify analytics but depend on immutable files, metadata coordination, and compaction as data changes. Continuous updates become increasingly expensive and operationally complex.

Infrastructure Creep

Streaming systems, warehouses, operational databases, vector stores, and synchronization pipelines quietly recreate the complexity the lakehouse was intended to eliminate.

Performance Tradeoffs

Organizations increasingly maintain separate analytical and operational systems because file-based lakehouses weren't designed for continuously updated analytical workloads.

We were usually bottlenecked in the past, getting maybe 10,000 rows per second. With an in-house solution on VAST, we are now ingesting over 1,000,000 rows per second. That makes a massive difference in achieving low data latency.

Aaron Pritchard
Technical Team Lead, Evodian
Why Now

Why Modern Lakehouses Need a Better Foundation

The modern lakehouse should remain open. Spark should remain Spark. Trino should remain Trino. Medallion architectures should remain familiar. What needs to change is the analytical data layer beneath them.

VAST replaces immutable analytical files with a native analytical database built on VAST’s Disaggregated Shared Everything (DASE) architecture. Existing analytical engines continue operating through familiar catalogs 
and APIs, while workloads requiring continuous updates, point lookups, 
and analytical scans gain the performance characteristics of a flash-native VAST DataBase rather than a file format.

Organizations can continue using Iceberg where it makes sense while adopting VAST DataBase for performance critical analytical workloads, 
or consolidate entirely as their lakehouse evolves.

Why Modern Lakehouses Need a Better Foundation
VAST AI Operating System

Modernize the Lakehouse Without Rebuilding It

Modernize your lakehouse without abandoning the open analytics ecosystem. VAST preserves Spark, Trino, SQL, Python, and familiar data engineering patterns while replacing file-based bottlenecks with a shared-everything architecture built for continuously updated analytics, higher concurrency, and dramatically simpler infrastructure.

Keep Your Open Ecosystem

Continue using Spark, Trino, Python, SQL, and existing lakehouse workflows while replacing file-based bottlenecks with a flash-native analytical data layer.

Continuously Updated Analytics

Replace immutable analytical files with a native database built for continuously updated data, high-concurrency point lookups, and large-scale analytical queries.

Simplify the Medallion Architecture

Run Bronze, Silver, and Gold pipelines on one governed platform with dramatically less copying, synchronization, and operational overhead.

Consolidate Lakehouse Infrastructure

Replace separate storage, streaming, operational databases, vector stores, and ETL infrastructure with one shared-everything platform.

The Three Pillars of the Modern Lakehouse

Preserve the Lakehouse Patterns 
Your Teams Already Know

Simplify Medallion Architecture

The Bronze, Silver, and Gold architecture remains the standard for organizing analytical data. VAST simplifies its implementation by keeping every stage on 
one governed platform, allowing Spark, Trino, SQL, and Python to transform and query shared data without unnecessary copying, synchronization, or separate 
storage systems.

Simplify Medallion Architecture

Modernize the Analytical Layer

Keep your data lake while modernizing the analytical layer above it. VAST DataBase replaces file-based analytical tables with a native database designed for continuously updated data, high-concurrency lookups, and large analytical scans, reducing the compaction and synchronization overhead of traditional lakehouse architectures.

Modernize the Analytical Layer

Unify the Analytics Stack

From ingestion through analytics, every stage runs on the same shared platform. Stream data, transform it with Spark, Trino, SQL, or Python, and query governed datasets without the infrastructure sprawl, duplication, and synchronization required by traditional lakehouse architectures.

Unify the Analytics Stack
The Modern Lakehouse, Powered by VAST DataBase

Why VAST DataBase?

VAST DataBase modernizes the analytical layer of the lakehouse. Rather than relying solely on 
file-based tables for structured analytics, it provides a native analytical database for workloads requiring continuously updated data, low-latency lookups, and high-concurrency SQL while preserving compatibility with Spark, Trino, Python, and the broader lakehouse ecosystem.

Continuously Updated Analytics

Support continuously updated analytical datasets as new data arrives, reducing the synchronization, ETL, and refresh workflows common in traditional lakehouses.

Native Analytical Storage

Replace file-based analytical tables with a native database built for continuously updated data, delivering dramatically higher update throughput and lower operational overhead.

High-Performance Point Lookups

Run operational analytics, feature stores, and retrieval workloads alongside large analytical scans without separate serving databases.

What does DataEngine help Enterprises do?

Extend the Lakehouse with DataEngine

Modern lakehouses require more than analytical storage. VAST DataEngine integrates event streaming, orchestration, managed compute, and serverless processing on the same platform, simplifying ingestion, transformation, and analytics while reducing the Kafka, ETL, and compute infrastructure traditionally deployed around the lakehouse.

Kafka API-Compatible Event Broker

Replace standalone Kafka clusters with native event streaming that integrates directly with analytical storage, making event data immediately available for analytics and AI.

Managed Spark & Trino

Run managed Spark and Trino services directly on VAST or continue using existing compute environments with the same catalogs, APIs, and analytics workflows.

Event-Driven Automation

Automatically trigger transformations, vectorization, quality checks, and downstream workflows as data arrives, reducing ETL complexity and operational overhead.

Unified Compute Platform

Run Spark, Trino, and Python serverless functions on the same platform as your data, minimizing movement while simplifying deployment and operations.

Use Cases

Common Lakehouse Modernization Projects

Organizations are modernizing lakehouse architectures to simplify infrastructure, reduce costs, and support continuously updated analytics. VAST provides a common platform for legacy modernization, operational analytics, AI, and large-scale data processing without disrupting existing workflows.