What’s inside
Most lakehouse architectures don’t fail. They get harder to use, because every new engine, team, and AI initiative adds another copy, catalog, or export. This technical whitepaper sets out five requirements an AI-ready lakehouse has to meet, then shows how AIStor meets each one, from an Iceberg REST catalog built into the storage layer to OpenSharing, which lets Databricks and other clients query on-prem tables in place. It closes with a checklist for verifying every claim on your own data.
What you’ll learn
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How to run every engine against one governed copy of your data with the full S3 API and a native Apache Iceberg REST catalog
How to share Delta and Iceberg tables with Databricks, Power BI, and Spark without copying them, and revoke access at the source
How four common starting points (Hadoop replacement, Databricks on on-prem data, observability tooling, and new AI initiatives) end up on the same foundation
What migration involves, including what runs unmodified, what takes work, and how to move incrementally
How to validate each requirement in your own environment with a hands-on checklist
