Enterprise AI depends on more than rows and columns. Documents, images, embeddings, logs, and text are joining the analytical data estate. Learn a framework for evolving the lakehouse without building a new platform for every modality.
Session Details
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One hour on the multimodal lakehouse, with time for your questions.
What changes when documents, images, embeddings, logs, and text sit alongside traditional analytical data.
How Apache Iceberg, Apache Polaris, Dremio, and emerging AI-native storage formats can work together for analytics, retrieval, machine learning, and agents.
A way to think about extending your lakehouse for the next generation of AI applications without a separate stack per model or modality.
