Part 7/10:

The speaker notes that Iceberg acts as a view of data, not a storage engine or execution engine. It provides a table interface via APIs, making data accessible via standard SQL or programmatic interfaces.

Building AI-Enhanced Data Pipelines

Moving beyond traditional querying, the presentation explores integrating AI capabilities directly into data workflows. Snowflake's architecture exemplifies this by layering:

• Data Storage & Querying: Using Iceberg tables on S3 with Snowflake as a powerful engine.

• Model Integration: Incorporating models like Anthropic, OpenAI, Meta, and others within a "model garden" to perform tasks such as sentiment analysis, document classification, or semantic search.