Course
Intro to Snowflake for Devs, Data Scientists, Data Engineers
BasicSkill Level
Updated 06/2026
SnowflakeData Engineering9 hr61 videos162 Exercises8,100 XPStatement of Accomplishment
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What you'll learn
- Differentiate Snowflake's architecture layers (storage, compute, services) and identify when to use Snowsight, worksheets, and the marketplace for data access.
- Determine the right table type (permanent, transient, temporary) and apply Time Travel, Zero-Copy Cloning, and Resource Monitors to manage data lifecycle and cost.
- Recognize how roles, warehouses, stages, and pipes work together to ingest data securely and grant least-privilege access across teams.
- Assess query performance using the query profile, caching behavior, and clustering, and apply tuning techniques to reduce credit consumption.
- Identify how Snowflake Cortex LLM functions, the Cortex CLI, and Streamlit in Snowflake combine to build generative AI applications directly on governed data.
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Start Course for FreePrerequisites
There are no prerequisites for this course1
Snowflake’s Core Objects and Architecture
After a very brief intro to the course, learners will create a free trial, open a worksheet, and query sample data. They’ll learn about scaling virtual warehouses and create a virtual warehouse to ingest Tasty Bytes data. They’ll learn about stages, databases, schemas, and tables. They’ll manipulate semi-structured data. They’ll also learn about the different Snowflake architectural layers.
2
Snowflake Feature Overview
Learners will identify a recently introduced “error” in the data and use time travel to correct it. They’ll learn about permanent, transient, and temporary tables, and cloning. They’ll create resource monitors. They’ll create UDFs, a UDTF, and a SQL stored procedure. They’ll learn about role-based access, the VS Code extension, Snowpark DataFrames, and the Snowflake CLI.
3
Overview of Builder Workloads: Data Engineering, AI / ML, Apps
Learners will explore four Snowflake workloads: Data Engineering, Generative AI, Machine Learning, and Applications. After reviewing each workload, they’ll see one aspect of that workload in practice: for DE, ingesting streaming data with Snowpipe; for GenAI, using the Snowflake Cortex LLM function “Complete”; for ML, using Snowpark ML to create an XGBoost model and make predictions about a food truck’s location; and for apps, running a Streamlit app that shows us Tasty Bytes’ daily revenue. They will then learn about the Snowflake Data Cloud.
Intro to Snowflake for Devs, Data Scientists, Data Engineers
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