Delivering AI ready data and analytics with a Unified Iceberg Lakehouse

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As artificial intelligence and data science initiatives take focus, organizations are under increasing pressure to ensure their data is AI-ready. To investigate how a unified lakehouse approach can address these challenges, Nucleus interviewed enterprises that adopted Dremio over the past 3 years and found significant benefits, including 61 to 73 percent cost savings on data reads, more predictable analytics spend, and improved data hygiene. By leveraging open table formats such as Apache Iceberg to support large data science, machine learning, and AI workloads, organizations reduced data engineering strain through robust metadata handling, schema evolution, and time-travel queries, while query acceleration “Reflections” optimize performance for advanced analytics and machine learning processes. Overall, leveraging this unified lakehouse approach, adopters better positioned themselves to handle the increasing demands for real-time analytics, unified governance, and flexible scale that increasingly define proper data utilization in 2025.

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