Course
Introduction to Modern Data Engineering with Snowflake
IntermediateSkill Level
Updated 06/2026
SnowflakeData Engineering5 hr34 videos70 Exercises3,500 XPStatement of Accomplishment
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What you'll learn
- Differentiate modern data engineering in Snowflake from legacy, siloed approaches, and explain how the Ingestion-Transformation-Delivery (ITD) framework structures a data pipeline.
- Determine which batch ingestion technique to use - the Snowflake Marketplace, Snowsight, the Snowflake CLI, or the COPY INTO command - and tune virtual warehouses for efficient loading.
- Identify the SQL and Snowpark tools for transforming data, including user-defined functions, stored procedures, streams, and Dynamic Tables.
- Evaluate the options for delivering data products, from Snowflake Marketplace sharing to Streamlit in Snowflake apps and Snowflake Native Applications.
- Recognize how tasks and directed acyclic graphs (DAGs) add automation and orchestration to continuous data pipelines.
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Start Course for FreePrerequisites
There are no prerequisites for this course1
Modern data engineering with Snowflake
Discover how the explosion of data gave rise to data engineering, and how Snowflake reshapes the modern approach. You'll frame your work around the Ingestion-Transformation-Delivery (ITD) framework, prepare your development environment, and build your first simple pipeline in Snowflake.
2
Batch data ingestion with Snowflake
Ingest data into Snowflake at scale using a range of powerful techniques. You'll load data through the Snowflake Marketplace, the Snowsight web interface, the Snowflake CLI, and the COPY INTO command, pull from external systems with native connectors, and tune virtual warehouses for efficient batch ingestion.
3
Data transformations with Snowflake
Transform raw data into analysis-ready data using SQL and Snowpark for Python. You'll create and apply user-defined functions, stored procedures, streams, and Dynamic Tables, and run transformations from inside Visual Studio Code using Snowflake's official extension.
4
Delivering data products with Snowflake
Turn clean, transformed data into data products that deliver value. You'll share datasets through the Snowflake Marketplace, build and share insights with a Streamlit in Snowflake application, and deploy a Snowflake Native Application using the Snowflake CLI.
5
Orchestrating continuous data pipelines with Snowflake
Bring your pipelines to life with orchestration. You'll automate pipeline steps using user-managed and serverless tasks, link tasks into a task graph (DAG), and execute individual tasks and entire DAGs to run continuous, automated data pipelines.
Introduction to Modern Data Engineering with Snowflake
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