Learning Center

Without a catalog, your Iceberg tables are just collections of structured but undiscoverable sets of files in object storage.

A data lakehouse is an architecture that combines the capabilities and benefits of both a data warehouse and data lake.

Learn how vectors power generative AI by turning text into high-dimensional numeric representations that enable semantic search, RAG, and intelligent applications.

Learn how managing infrastructure through code—using version control, automation, and repeatable configurations—brings reliability, speed, and fewer human errors.

Understand how sovereign cloud ensures data remains under a nation’s laws and infrastructure control to meet legal, security, and regulatory demands.

Explore how a dedicated cloud environment offers organizations stronger control, security, and performance benefits for demanding workloads.

Concrete examples of organizations using lakehouse architectures—combining data lake scale with data warehouse performance—to solve modern analytics problems.

Discover the seven essential storage capabilities enterprises need—from low latency and durability to full S3 compatibility and software-defined flexibility—to support all forms of AI.

Explore real world AI applications—from content generation to autonomous systems—and how high-performance storage underpins them.

The stages of machine learning (training, fine-tuning, serving, etc.) and what kinds of storage and architecture each demands.

Learn how object storage is evolving across enterprises to meet AI needs in 2025.