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The rise of artificial intelligence (AI) has reshaped the way enterprises think about data. AI agents, machine learning models, and modern analytics all depend on timely access to high-quality, well-governed data. This is why the data lakehouse architecture has become so critical, as it unifies the flexibility and scalability of data lakes with the reliability and governance of data warehouses. By doing so, it not only reduces costs but also ensures that AI tooling can operate on enterprise-wide data in a seamless and governed manner.
With more organizations moving toward this architecture, Apache Iceberg has emerged as the open table format at the center of the modern lakehouse. Iceberg provides the foundation for consistent, scalable, and interoperable data storage across multiple engines.
As outlined in Architecting an Apache Iceberg Lakehouse (Manning, 2025), practitioners should apply five high-level tips to designing and implementing an Iceberg-based lakehouse; thereby, approaching their lakehouse journey with clarity and confidence. These include:
- Conduct an Architectural Audit
Before choosing tools or building pipelines, the most crucial step is to understand where to begin. This means conducting an architectural audit. To start, meet with stakeholders such as data engineers, analysts, business users, and compliance teams to collect a clear picture of how data is currently used. Ask questions like:
- Where are the biggest bottlenecks in accessing and analyzing data?
- What governance or compliance requirements must be met?
- How is data shared across business units today, and what limitations exist?
By consolidating this knowledge, organizations can build a requirements document that captures the functional and non-functional needs of the organization. The resulting document will then serve as the north star throughout the design process, keeping the team focused on solving the correct problems rather than chasing every shiny new feature vendors will present.
- Build a Local Prototype
Once requirements are defined, the next step is to experiment in a safe, local environment. For instance, prototyping on a laptop is easy thanks to open-source technologies/capabilities like these:
Dremio Community Edition or Trino OSS for querying and federating data.
- MinIO for providing an S3-compatible object store.
- Project Nessie for data-as-code catalog functionality.
- Apache Iceberg itself serves as the foundational table format.
By setting up a mock lakehouse on a laptop or in a small dev environment, data engineers can gain a hands-on understanding of how the pieces fit together. This also helps them visualize the end-to-end flow of data, from ingestion to governance to analytics, before having to make large-scale architectural decisions. The lessons learned will also help during prototyping by giving them confidence and clarity when it comes time to scale.
3: Compare Vendors Against Your Requirements
When ready to evaluate vendors, it’s easy to get swept up in flashy demos and marketing claims. Vendors will emphasize the strengths of their platform, but those strengths may not actually align with what the organization actually needs.
Again, this is where the requirements document becomes invaluable. Instead of letting vendors define the conversation, the earlier defined requirements will serve as a cognitive filter. Ask each vendor to demonstrate how they meet the specific needs identified, such as governance, cost efficiency, or AI-readiness, rather than simply showcasing their broadest feature set.
This approach not only saves time but also ensures that the business is building a lakehouse that solves the organization’s problems, not one optimized for someone else’s priorities. Remember, the right vendor isn’t the one with the longest feature list, but the one whose capabilities map most closely to the requirements uncovered during the architectural audit.
4: Master the Metadata Tables
Apache Iceberg isn’t just about scalable tables; it also provides metadata tables that give deep visibility into the state of the business’ data. These include tables that show snapshot history, file manifests, partition statistics, and more. By learning how to query and interpret these metadata tables, data professionals can:
- Monitor table health and detect issues early.
- Identify when compaction, clustering, or cleanup jobs are actually needed.
- Replace rigid maintenance schedules with intelligent, event-driven maintenance based on real-time conditions.
For example, rather than compacting files every night at midnight, organizations might use metadata tables to trigger compaction only when small files accumulate beyond a threshold. This kind of adaptive optimization helps keep costs under control while maintaining consistently high performance. Mastering Iceberg’s metadata is one of the most potent ways to operate the lakehouse efficiently, transforming routine maintenance into a smarter, data-driven process.
5: Position the Business for the Polaris Future
A data lakehouse catalog or metadata catalog is the backbone of any Iceberg lakehouse. It determines how tables are organized, governed, and accessed across engines. Today, many vendors are already adopting or integrating with Apache Polaris, the open-source catalog built on the Iceberg REST protocol.
Numerous vendors have announced Polaris-based Catalog offerings ,and more are following closely behind. This momentum signals that Polaris is on track to become the industry-standard catalog for Iceberg-based architectures. This means if you’re self-managing, deploying Polaris can ensure future interoperability. Should the business prefer a managed solution, it’s important to select a vendor that already provides a Polaris-based catalog.
By aligning the lakehouse catalog strategy with Polaris, you’re not only solving today’s challenges but also preparing for an ecosystem where interoperability and cross-engine consistency are the norm. This foresight will ensure your architecture scales gracefully as the Iceberg ecosystem matures.
TLDR? Here are the Highlights…
Architecting a modern data lakehouse isn’t just about technology; it’s about thoughtful design, planning, and execution. Apache Iceberg provides the foundation for building a scalable, governed, and interoperable lakehouse, but success depends on how organizations approach the journey. Considerations include:
Start with an architectural audit to ground the design in real organizational needs.
- Prototype locally to build intuition and confidence before scaling.
- Evaluate vendors against requirements, not against their marketing.
- Leverage Iceberg’s metadata tables for intelligent maintenance and optimization.
- Future-proof the catalog strategy by aligning with Polaris.
These five tips only scratch the surface of what’s possible. The organizations that succeed in the AI era will be those that treat data as a strategic asset, accessible, governed, and optimized for both human and machine intelligence. With Apache Iceberg at the core of the lakehouse, and a thoughtful architecture behind it, organizations will be ready to meet that challenge head-on.
About the Author: Alex Merced is the co-author of “Apache Iceberg: The Definitive Guide” and Head of Developer Relations at Dremio, providers of the leading, unified lakehouse platform for self-service analytics and AI. With experience as a developer and instructor, his professional journey includes roles at GenEd Systems, Crossfield Digital, CampusGuard, and General Assembly. He co-authored “Apache Iceberg: The Definitive Guide” published by O’Reilly and has spoken at notable events such as Data Day Texas and Data Council.