About This Course

Building with Snowflake moves a data engineer from operating a Snowflake account to building on it. The prerequisite course, Platform Governance and Performance, covers account structure, access control, and warehouse performance, and this course assumes that foundation rather than repeating it. It focuses instead on getting data in continuously and reliably, keeping transformed data fresh without manual intervention, and building advanced dbt pipelines that reach all the way into Snowflake Cortex's generative AI functions. Every subsection opens with a short unit pointing at an official Snowflake or dbt Labs course, workshop, or guide, then continues with original material that goes further than that vendor content alone, so a subsection is never just a link out and never just a lecture invented from nothing.

The first half of the course covers continuous ingestion, pipeline resiliency, and declarative transformation. It compares Snowpipe and Snowpipe Streaming for real time and batch loading, works through semi-structured data and external stages, and builds continuous change data capture pipelines with Streams and Tasks. From there it covers handling bad data with Event Tables and Alerts, designing a dead letter queue for streaming ingestion, and automatic schema evolution, before moving into Dynamic Tables: what target lag actually promises, how to troubleshoot a stalled refresh, and how to align clustering between a base table and the Dynamic Table built on it.

The second half turns to advanced dbt architecture on Snowflake. It covers choosing and tuning incremental strategies and clustering keys, then works through a hands on lab comparing Dynamic Tables against dbt's own materialized dynamic table pattern, including when dbt should own the refresh schedule and when Snowflake should. From there it covers calling Snowflake Cortex AI SQL functions, both directly in a query and from inside a dbt model, along with the defensive practices that non deterministic model output requires, and closes with dbt Mesh: splitting a project across teams with groups and access modifiers, and enforcing model contracts so a public model's shape cannot change without warning.

Who This Course Is For

This course assumes the account structure, security, and performance fundamentals covered in Platform Governance and Performance, or equivalent hands on Snowflake experience. It also assumes comfort writing SQL and, for the second half of the course, basic familiarity with how a dbt project is organized. It does not assume any prior exposure to Snowpipe Streaming, Dynamic Tables, dbt Mesh, or Cortex AI functions, since those are exactly what the course teaches.

How the Course Is Structured

The course is organized into two sections. Continuous Ingestion, Pipelines and Resiliency covers five subsections: Real-Time and Batch Ingestion, Semi-Structured Data and External Stages, Continuous CDC with Streams and Tasks, Error Handling and Schema Drift, and Declarative Pipelines with Dynamic Tables. Advanced dbt Architecture on Snowflake covers four: Custom Incremental Strategies and Clustering, Hands-On Lab and Dynamic Tables versus dbt, In-Pipeline GenAI Enrichment, and Data Mesh and Scaling Transformations. Each subsection opens with a course link unit pointing to the relevant vendor material and continues with two to four original units that go past where that vendor content stops.

Time Commitment and Completion

Plan for a few focused hours per subsection. Two of the linked workshops and the hands on lab unit are longer and are meant to be worked through slowly across more than one sitting rather than rushed through in one. Completion is tracked automatically: HTML units complete on view, and the course outline and Progress tab reflect progress as each unit is opened.