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Shoebur Rahman調達スペシャリスト、Giz Bangladesh
コースの説明
This course introduces learners to Snowflake as a platform for building applications, data pipelines, and AI models and workflows. It takes them from zero Snowflake knowledge all the way to creating user-defined functions, using a Snowflake Cortex LLM function, editing a Streamlit app, and more.The course unfolds in three parts: First, participants learn to use Snowflake’s core objects such as virtual warehouses, stages, and databases. Then they learn about slightly more advanced objects and features such as time travel, cloning, user-defined functions, and stored procedures. Finally, they’re introduced to Snowflake’s capabilities for data engineering, generative AI, machine learning, and app development.Learners come away equipped to start building with Snowflake and to continue their Snowflake learning journeys. This course is a prerequisite for upcoming Snowflake courses on data engineering, AI, and apps.
前提条件
このコースに前提条件はありません
カリキュラム
コース概要
1
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.
- Intro and Course Overview50 XP
- Course Components and Resources50 XP
- Worksheets and a Simple Example - Part I50 XP
- Worksheets and a Simple Example - Part II50 XP
- Code to Run Before Hands-on Assignment — Worksheets50 XP
- Worksheets and a Simple Example — Question 150 XP
- Worksheets and a Simple Example — Question 250 XP
- Virtual Warehouses Overview50 XP
- Virtual Warehouses Overview - What's Changed50 XP
- Virtual Warehouses Scaling - Part I50 XP
- Virtual Warehouses Scaling - Part II50 XP
- Virtual Warehouses Scaling - What's Changed50 XP
- Virtual Warehouses — Question 150 XP
- Virtual Warehouses — Question 250 XP
- Virtual Warehouses — Question 350 XP
- Stages and Basic Ingestion - Part I50 XP
- Stages and Basic Ingestion - Part II50 XP
- Stages & Basic Ingestion - What's Changed50 XP
- Code to Run Before Hands-on Assignment — Stages50 XP
- Stages and Basic Ingestion — Question 150 XP
- Stages and Basic Ingestion — Question 250 XP
- Stages and Basic Ingestion — Question 350 XP
- Databases and Schemas - Part I50 XP
- Databases and Schemas - Part II50 XP
- Database Explorer50 XP
- Databases and Schemas — Question 150 XP
- Databases and Schemas — Question 250 XP
- Databases and Schemas — Question 350 XP
- Tables - Part I50 XP
- Tables - Part II50 XP
- Dynamic Tables50 XP
- Code to Run Before Hands-on Assignment — Tables50 XP
- Tables — Question 150 XP
- Tables — Question 250 XP
- Tables — Question 350 XP
- Views - Part I50 XP
- Views - Part II50 XP
- Views — Question 150 XP
- Views — Question 250 XP
- Views — Question 350 XP
- Semi-Structured Data Types50 XP
- Semi-Structured Data Manipulation50 XP
- Semi-Structured Data — Question 150 XP
- Semi-Structured Data — Question 250 XP
- Semi-Structured Data — Question 350 XP
- Snowflake Architecture Overview50 XP
- Snowflake Architecture Overview - What's Changed50 XP
- Wrap-up of Snowflake’s Core Objects and Architecture50 XP
- Chapter 1 review - Question 150 XP
- Chapter 1 review - Question 250 XP
- Chapter 1 review - Question 350 XP
- Chapter 1 review - Question 450 XP
- Chapter 1 review - Question 550 XP
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.
R
Intro to Snowflake for Devs, Data Scientists, Data Engineers
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