How to Choose the Right Data Architecture for Analytics and AI at Scale

AI, real-time apps, and distributed data have exposed the limits of centralized warehouses and brittle pipelines.

This guide shows you how to evaluate modern lakehouse platforms, avoid vendor lock-in, and build an open, governed architecture that scales across cloud, hybrid, and on-premises environments.

Download the free buyer’s guide and make your next platform decision with confidence.

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What You’ll Learn

Inside this practical buyer’s guide, you’ll discover:

• How to evaluate lakehouse vendors using a proven checklist
• What “Iceberg-compatible” really means (and what to verify in a POC)
• How to support BI dashboards, AI training, and real-time workloads on the same platform
• How to eliminate data duplication and reduce total cost of ownership
• How to implement governed, federated access without slowing teams down
• A step-by-step roadmap to move from pilot to production in 90 days

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Who This Guide Is For

Built for enterprise teams modernizing their data stack:

• CTOs & CIOs
• Data platform & engineering leaders
• AI/ML teams
• Enterprise architects
• Analytics leaders

If you’re evaluating lakehouse platforms or planning an AI-ready data foundation, this guide is for you.

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Why This Guide Is Different

Most content explains what a lakehouse is. This guide helps you choose the right one.

Inside, you’ll find 

• A practical vendor evaluation framework
• Clear criteria for architecture decisions
• Questions to ask during proofs of concept
• Common pitfalls and trade-offs to watch for
• A pragmatic adoption roadmap from pilot to production

Ready To Evaluate Your Lakehouse Options?

Download the guide now.