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The Expedition lab recordings are on demand. Build along in a free trial account, pass the auto-grader, and earn a badge to share on social media. Missed Snowflake Expedition, or started a lab and didn’t finish?
Streaming tables and materialized views, read in place as Iceberg: the settings that matter, what the logs show, and the Snowflake errors I hit Most guides to reading Databricks from Snowflake start with a plain Delta table.
Caller’s rights, CALLER grants, and the shared objects that refuse to cooperate Most data platforms hit a governance problem right when they start working. The dbt models ship, the semantic views exist, the dashboards render.
Snowflake Semantic Views provide a governed, reusable layer that defines how tables relate, what metrics mean, and who can access what.
At the Expedition 2026 event, Snowflake unveiled a sweeping architectural evolution, transitioning from a cloud data platform into a unified engine for autonomous AI agents, real-time streaming applications, and conversational business...
Data access, training cost, CI/CD readiness, and business needs decide how your models reach production, and shape your environments, your pipelines, and your risk.
Introduction In enterprise data work, the word “ontology” comes up more and more. The idea is to give an AI agent an ontology as the context layer it refers to when it works with company data.
A Snowflake Architect’s guide to designing data that AI agents can actually understand Every few weeks someone asks me the same thing: “We want a Cortex Agent on top of our data.
Blogsby Glyph