After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
课程介绍
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.
先修要求
本课程无先修要求
课程大纲
课程大纲
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 经验值
- Course Components and Resources50 经验值
- Worksheets and a Simple Example - Part I50 经验值
- Worksheets and a Simple Example - Part II50 经验值
- Code to Run Before Hands-on Assignment — Worksheets50 经验值
- Worksheets and a Simple Example — Question 150 经验值
- Worksheets and a Simple Example — Question 250 经验值
- Virtual Warehouses Overview50 经验值
- Virtual Warehouses Overview - What's Changed50 经验值
- Virtual Warehouses Scaling - Part I50 经验值
- Virtual Warehouses Scaling - Part II50 经验值
- Virtual Warehouses Scaling - What's Changed50 经验值
- Virtual Warehouses — Question 150 经验值
- Virtual Warehouses — Question 250 经验值
- Virtual Warehouses — Question 350 经验值
- Stages and Basic Ingestion - Part I50 经验值
- Stages and Basic Ingestion - Part II50 经验值
- Stages & Basic Ingestion - What's Changed50 经验值
- Code to Run Before Hands-on Assignment — Stages50 经验值
- Stages and Basic Ingestion — Question 150 经验值
- Stages and Basic Ingestion — Question 250 经验值
- Stages and Basic Ingestion — Question 350 经验值
- Databases and Schemas - Part I50 经验值
- Databases and Schemas - Part II50 经验值
- Database Explorer50 经验值
- Databases and Schemas — Question 150 经验值
- Databases and Schemas — Question 250 经验值
- Databases and Schemas — Question 350 经验值
- Tables - Part I50 经验值
- Tables - Part II50 经验值
- Dynamic Tables50 经验值
- Code to Run Before Hands-on Assignment — Tables50 经验值
- Tables — Question 150 经验值
- Tables — Question 250 经验值
- Tables — Question 350 经验值
- Views - Part I50 经验值
- Views - Part II50 经验值
- Views — Question 150 经验值
- Views — Question 250 经验值
- Views — Question 350 经验值
- Semi-Structured Data Types50 经验值
- Semi-Structured Data Manipulation50 经验值
- Semi-Structured Data — Question 150 经验值
- Semi-Structured Data — Question 250 经验值
- Semi-Structured Data — Question 350 经验值
- Snowflake Architecture Overview50 经验值
- Snowflake Architecture Overview - What's Changed50 经验值
- Wrap-up of Snowflake’s Core Objects and Architecture50 经验值
- Chapter 1 review - Question 150 经验值
- Chapter 1 review - Question 250 经验值
- Chapter 1 review - Question 350 经验值
- Chapter 1 review - Question 450 经验值
- Chapter 1 review - Question 550 经验值
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
课程完成
