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.
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