This end-to-end demo walks through AIStor Table Sharing from setup to live SQL query in Databricks. Starting in the AIStor console, the demo covers creating an Iceberg warehouse, loading structured data using Python, and configuring a Table Share with scoped bearer token authentication. The share is then imported into Databricks as a new catalog, where column types and sample data are verified against the source before running SQL joins across the shared tables. The session also demonstrates live share maintenance: adding a new table to an active share without modifying the existing token or interrupting any connected Databricks session. For teams also running Spark on-premises, the same share profile is used without additional configuration, confirming unified access from both environments.
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Tables can be added to an existing share from the AIStor console at any time without generating a new token or interrupting active Databricks consumers.
A single Table Share endpoint serves both Iceberg and Delta Lake tables simultaneously, with the consumer needing only the share profile file and a bearer token to connect.
The same share profile works identically for cloud-based Databricks and local Apache Spark, giving on-premises and cloud analytics platforms unified access to AIStor data without separate configurations.
Data engineers and platform administrators evaluating how to give Databricks or on-premises Spark access to Iceberg data in AIStor without building replication pipelines or moving data to the cloud.
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