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Data Pipeline Automation in Snowflake

BasicSkill Level
Updated 05/2026
Load, automate, and optimize data pipelines in Snowflake using COPY INTO, Snowpipe, streams, tasks, dynamic tables, and query performance tools.
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SnowflakeData Engineering
3 hr
15 videos
48 Exercises
3,350 XP
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Course Description

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.

Prerequisites

There are no prerequisites for this course
1

Data Loading, Ingestion, and Connectivity

Master Snowflake's data ingestion pipeline — from staging files through COPY INTO, Snowpipe, and Snowpipe Streaming — and connect Snowflake to the broader data ecosystem through connectors, drivers, and export tools.
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2

Pipeline Orchestration and Data Objects

Build robust data pipelines using Snowflake-native orchestration — streams for change capture, tasks for scheduling, dynamic tables for declarative transformation, and external and Iceberg tables for open-format storage.
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Data Pipeline Automation in Snowflake
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