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A Data Leader's Guide: Evolving Your Data Lakehouse for AI

This guide reveals how leading enterprises achieve significant cost reductions and performance improvements by unifying their data infrastructure with Apache Iceberg and object-native storage. Get proven strategies, reference architectures, and real-world case studies for building a lakehouse for analytics and AI at scale.

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What's Inside?

Your Complete Data Lakehouse Guide

Apache Iceberg integration strategies — How native Iceberg implementation unifies structured and unstructured data tables into a single, coherent data fabric
Object-native storage architecture — Why ONS provides virtually unlimited scalability at low per-gigabyte prices while eliminating traditional capacity constraints
AI stack requirements and convergence — Understanding how predictive AI, generative AI, and agentic AI systems depend on lakehouse architecture for real-time data access
Proven enterprise performance metrics — Real-world case studies showing 80% cost reductions and 33% performance improvements in data infrastructure
Hybrid and multi-cloud deployment models — Reference architectures for batch-centric, real-time, and multi-engine lakehouses across public, private, and edge environments
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Table of Contents listing sections and page numbers for a guide on evolving data lakehouse for AI, including topics like data lakehouse basics, Apache Iceberg, data storage solutions, AI adoption, use cases, and conclusion.
Table of Contents
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Introduction text explaining the evolving role of data marts, warehouses, and lakes, highlighting key pressures like rapid growth, fast technical evolution, and expanding AI stack requirements. A sidebar shows statistics: 33% report high data preparation costs, 36% struggle with governance, 41% of IT leaders moved from traditional warehouses, 85% use data lakehouses for AI, and 67% plan analytics on lakehouses by 2028. Below, a bar chart shows worldwide data storage by type (block, file, object) increasing from 2018 to 2026.
Introduction
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Page explaining what a data lakehouse is, highlighting that it unifies data warehouses and data lakes by merging flexibility, scale, reliability, and governance for handling structured, semi-structured, and unstructured data with features like ACID transactions and schema enforcement. It contrasts warehouses, lakes, and lakehouses and includes a boxed definition from Google Cloud Services.
What is a Data Lakehouse?
What You'll Learn

Master Modern Lakehouse Architecture

Open Table Formats
Choosing the Right Format
How to evaluate and implement Apache Iceberg, Delta Lake, and Apache Hudi—with decision criteria for choosing the right open table format for your lakehouse architecture
Migration Strategy
From Fragmented to Unified
Strategies for migrating from fragmented data warehouses and lakes to a unified object-native storage layer that supports both structured and unstructured data at exabyte scale
Architecture Patterns
Lakehouse Design Models
Reference architectures for batch-centric, hybrid real-time, and multi-engine lakehouses—including integration patterns for Spark, Trino, Flink, and modern query engines
Iceberg Integration
Simplify Data Management
How to unify structured and unstructured data using Apache Iceberg integration—eliminating the complexity of managing separate catalogs and security models
Cost Optimization
Storage Economics
Why object-native storage architecture delivers virtually unlimited scalability at <$3/TB/month while traditional systems cost $10-30/TB
AI Deployment
Supporting AI at Scale
Proven deployment models for supporting predictive AI, generative AI, and agentic AI systems that require real-time access to enterprise data at scale
Industry Leader

Trusted by the Fortune 100

77% of Fortune 100 companies rely on MinIO AIStor for their AI/ML and analytics workloads

Ready to Build Your AI Data Lakehouse?

Join the 85% of organizations optimizing their data lakes and warehouses for AI. Download our Data Leader's Guide to learn how leading organizations are evolving to data lakehouses.
Get the Guide
What's Inside?

Your Complete Data Lakehouse Guide

80%

Cost reduction vs. legacy systems

33%

Faster query performance

250PB

Daily data ingest per client

20+

TiB/s throughput achieved
Native Apache Iceberg Integration
Unified catalog and storage without separate databases to manage or synchronize
Multi-Engine Compatibility
Works seamlessly with Spark, Trino, Flink, and all modern query engines
Hybrid Cloud Deployment
Supports hybrid environments using a 100% S3-compatible API so you can deploy and run anywhere
Real-Time Data Streaming
Native support for Kafka, Flink, and RabbitMQ enables continuous data pipelines from edge to AI
Enterprise Governance
ACID transactions, schema evolution, and fine-grained access control across all workloads for easier security and governance processes
Key Capabilities

Purpose-Built for AI & Analytics

Unified Data Foundation

Combine structured and unstructured data in a single system with native Iceberg integration, eliminating data silos that slow analytics and AI development.

Exabyte-Scale Throughput

Proven at 1+ exabyte with 20+ TiB/s throughput. Handle the largest enterprise data volumes with warehouse-level query performance on lake economics.

Apache Iceberg Native

Built-in support for Apache Iceberg with schema evolution, time travel, and ACID transactions. Plus compatibility with Delta Lake and Hudi.

Optimized for AI/ML Workloads

GPU-optimized data access with native feature store integration. Works with PyTorch, TensorFlow, MLflow, Kubeflow, and all major ML frameworks.
Industry Leader

Trusted by the Fortune 100

77% of Fortune 100 companies rely on MinIO AIStor for their AI/ML and analytics workloads

Ready to Build Your AI Data Lakehouse?

Join the 85% of organizations optimizing their data lakes and warehouses for AI. Download our Data Leader's Guide to learn how leading organizations are evolving to data lakehouses.
Get the Guide