Deploying a Python Application in Snowflake Hands-On

Name: Deploying a Python Application in Snowflake Hands-On
Rating: 4.7 (14 reviews)

Learn to design and deploy different application architectures in Snowflake using Python and SQL

Created byCristian Scutaru

Last updated 5/2026

English

What you'll learn

How to properly deploy an application into the Snowflake AI Data Cloud in multiple ways.
How to visualize the different building blocks of a data application.
How to think in terms of system architecture, modularity and scalability, when building and deploying a data application.
How to implement simple business logic in Python and get the code executed by the Snowflake SQL engine.

Course content

3 sections • 31 lectures • 4h 48m total length

Course Structure and Content2:23
Explore the course structure and content from SQL data generation to Python ETL and Snowpark dataframes. Build end-to-end Snowflake apps with VSCode extensions, notebooks, container runtimes, and Streamlit deployments.
How to Enjoy this Course2:36
Frequently Asked Questions1:33

Synthetic Data in SQL Worksheets19:02
Generate synthetic test data in Snowflake SQL worksheets using a table generator and random values, then save results to a fake customers table and explore related built-in options.
Fake Realistic Local Data with Python16:15
Let me share with you...1:29
Snowflake Extension in VSCode8:53
Load a CSV into Snowflake using the web UI data load wizard to create a table from file, then connect with the VSCode extension to run SQL and preview data.
Snowflake Connector for Python11:10
Snowpark Sessions10:37
Snowpark DataFrame API11:35
Explore how the Snowpark data frame API translates Python operations into SQL behind the scenes, materializing data with save as table and applying ELT transformations.
Let me share with you...2:16
Discover how to migrate from in-memory Pandas pipelines to Snowpark data frames and CTAS-based server-side transformations for large production data.
Test Your Knowledge
Python Worksheets8:32
Learn how Python worksheets in Snowflake convert Python code into dynamic stored procedures that run on the server, and compare them to SQL worksheets and their limitations.
Python Stored Procedures6:05
Declare and run Python stored procedures in Snowflake, executing within the warehouse via snowpark sessions, and learn the architecture, libraries, data type mappings, and SQL and Python requirements.
Snowpark Stored Procedures9:33
Discover how Snowpark stored procedures and UDFs let you decorate Python functions into SQL constructs. Explore stages, Python sandbox execution, and the tradeoffs between data frame and stored procedure APIs.
Let me share with you...1:26
Learn how a remote data architect built a Snowflake data ingestion pipeline with JavaScript stored procedures, the limitations of Python then, and how Python workflows emerged later.
Execute as Caller10:32
Explore how execute as owner versus execute as caller affects stored procedures in Snowflake, including granting rights to schemas, databases, and warehouses, and resolving privilege issues.
Test Your Knowledge
Jupyter Notebooks in VSCode9:36
Set up jupyter notebooks in vscode with the jupyter extension and create ipynb files. Select a kernel in a virtual environment to run snowpark workflows with python against snowflake.
Snowflake Notebooks10:36
Discover how to create and use Snowflake notebooks—from grid, repository, or import options—to run Python and SQL in a warehouse, and visualize data with plots.
Notebooks in Container Runtime15:09
Run Python in Snowflake notebooks with container runtime, selecting CPU or GPU compute pools and installing packages via pip. Explore container services, external access integrations, and differences from warehouses.
Test Your Knowledge
Streamlit Local Applications15:18
Learn to build Streamlit data apps connected to Snowflake via Snowpark, using input widgets, tabs, and data frames with a local web server and optional Streamlit cloud deployment.
Streamlit Multi-Page Applications10:27
Explore building a multi-page Streamlit app for Snowflake by organizing code into a pages folder, separating data generation, data inspection, and charts, and sharing parameters via utils and secrets.
Streamlit Cloud Applications12:04
Let me share with you...1:19
Streamlit in Snowflake Apps13:13
Streamlit App Deployments14:14
Deploy Streamlit apps to Snowflake via a stage and SQL script, automating dependencies with an environment YAML file and conda for scalable, multi-file, multi-page apps.
Test Your Knowledge
Secure Data Shares10:32
Master secure data sharing in Snowflake by creating outbound data shares and using private data exchange or marketplace to share secure views, UDFs, and UDTFs.
Snowflake Native Apps20:01
Examine how Snowflake native apps and data sets are exposed via the marketplace or private data exchange, using application packages and manifest YAMLs to share with consumer accounts.
Snowpark Container Applications27:22
Let me share with you...2:16
Explore deploying a Python application in Snowflake with containers and Snowpark container services. Assess Docker image loads, Kubernetes functionality, and basic scalability via compute pools and service instances.
Test Your Knowledge

Requirements

Basic Python programming skills
Basic knowledge of SQL
Some familiarity with the Snowflake platform

Description

This course will take one simple ETL/ELT piece of Python/SQL code and deploy it in over a dozen different ways, in Snowflake or connected to Snowflake. Each time describing the system architecture and the implications. On scalability, data protection and security, how close to the data the code runs.

Who this course is for

Python developers looking to extend their knowledge of Snowflake.
Aspiring Data Architects, with focus on Snowflake.
Solution Architects with a goal of understanding all sorts of Snowflake application development.
Data Engineers looking to move into Data Architecture.
Any technical person willing to better understand all sorts of architectures in Snowflake AI Data Cloud.

What you will learn

How to properly deploy an application into the Snowflake AI Data Cloud in multiple ways.
How to implement simple business logic in Python and get the code executed by the Snowflake SQL engine.
How to get from a simple Streamlit local web app to a complex Native App running in Snowflake Containers.
How to think in terms of system architecture, modularity and scalability, when building and deploying a data application.
How to visualize the different building blocks of a data application.
How to generate fake data with either built-in Snowflake functions or Python libraries.

What kind of architectures we'll present here

SQL Worksheets and Python Worksheets
Snowflake Connector for Python
Snowpark DataFrame API and Snowpark for stored procs
Pandas DataFrame API
Stored Procedures in Python and Execute as Caller
Jupyter Notebooks and Snowflake Notebooks
Streamlit Web Apps and Streamlit Community Cloud
Streamlit in Snowflake Applications
Secure Data Sharing
Snowflake Native Apps
Snowpark Container Services
VSCode Extensions for Snowflake and Jupyter

[Disclaimer: We are not affiliated with or endorsed by Snowflake, Inc.]

Who this course is for:

Python developers looking to extend their knowledge of Snowflake.
Aspiring Data Architects, with focus on Snowflake.
Solution Architects with a goal of understanding all sorts of Snowflake application development.
Data Engineers looking to move into Data Architecture.
Any technical person willing to better understand all sorts of architectures in Snowflake AI Data Cloud.

Deploying a Python Application in Snowflake Hands-On

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 7min

Content26 lectures • 4hr 40min

Conclusion2 lectures • 3min

Requirements

Description

Who this course is for: