Data Pipeline Automation in Snowflake

Master the tools and techniques for building reliable, automated data pipelines in Snowflake. You'll start by learning how to ingest data at scale — configuring stages and file formats, loading data with COPY INTO, and choosing between batch loading, Snowpipe, and Snowpipe Streaming for different latency and volume requirements.
From there, you'll build end-to-end pipeline orchestration skills: capturing row-level changes with streams, chaining multi-step workflows with task DAGs, and creating declarative, auto-refreshed pipelines with dynamic tables. You'll also learn when to reach for external and Iceberg tables for multi-engine and cloud-native scenarios.
The second half of the course sharpens your querying and transformation skills — extracting and unnesting semi-structured JSON from VARIANT columns, applying grouping extensions and window functions for advanced analytics, and encapsulating reusable logic in UDFs and stored procedures.
Finally, you'll tackle query performance: reading Snowflake's Query Profile to pinpoint bottlenecks, selecting the right optimization tool — Search Optimization, Query Acceleration Service, clustering keys, or materialized views — and writing cache-friendly SQL that avoids the common anti-patterns that silently degrade performance.