Intro to Snowflake for Devs, Data Scientists, Data Engineers

Go to Course: https://www.coursera.org/learn/snowflake-intro-app-developers-data-scientists-data-engineers

Introduction

Create and manipulate Snowflake's core objects, such as virtual warehouses, databases, schemas, tables, and stages.

Use important Snowflake features and objects, such as time travel, cloning, resources monitors, UDFs, stored procedures, and Snowpark DataFrames.

Understand the basics of Snowflake’s capabilities for data engineering, generative AI, machine learning, and app development.

Syllabus

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.

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.

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.