- I was the only Snowflake technical expert from Canada selected for their Data Superhero program in Jan 2022.
- Former SnowPro Certification SME (Subject Matter Expert) – many exam questions have been created by me.
- Passed four SnowPro certification exams to date (with no retakes): Core, Architect, Data Engineer, Data Analyst.
- Dozens of other certifications in Data Science and Machine Learning, Cloud Solution Architectures, Databases, etc.
- Dozens of apps designed and implemented with Streamlit and Snowflake on my blog on Medium.
- Specialized in Snowflake for several years, I served dozens of clients and implemented many real-life projects.
What You Will Learn
- How to create simple to complex web applications in Streamlit.
- How to deploy for free local Streamlit web apps to the Streamlit Community Cloud.
- How to connect to Snowflake from Streamlit apps, through either the Python Connector or a Snowpark session.
- How to use the DataFrame API and push Python code as stored procedure with Snowpark.
- How to extend Snowflake’s capabilities, with a hierarchical data viewer and a hierarchical metadata viewer.
- How to prototype with Streamlit apps data science, machine learning and data analysis scenarios.
- How to deploy a Streamlit web app as a Streamlit in Snowflake App.
- How to deploy a Streamlit web app as a Snowflake Native App.
- How to use the Snowflake Native App Framework to build or use apps with Streamlit.
- We’ll build several apps in Python from scratch, we’ll then convert them to local single or multi-page Streamlit web apps, deploy and share them on the Streamlit Community Cloud, deploy them in Snowflake as stored procs or Streamlit Apps, share them as Native Apps with other Snowflake accounts…
What Streamlit Areas You Will Learn About
- Input and Output Controls (Interactive Widgets, Display Text controls, etc.).
- Layout Components (sidebar, container, expander, tabs, etc.) and Forms.
- Events and Page Reruns.
- Data Caching, Session State and Callbacks.
- Theming and Configuration, TOML Secrets.
- First half of the course will be an end-to-end complete Streamlit bootcamp, with everything you need to know about Streamlit.
What Snowflake Areas You Will Learn About
- Creating a free Snowflake account and using the Snowflake web UI at the basic level.
- Connecting to Snowflake with SnowSQL, and executing SQL scripts with this command-line interface.
- Connecting to Snowflake with the Snowflake Connector for Python.
- Connecting to Snowflake with Snowpark for Python.
- Using Snowpark to push Python code as stored procedures.
- Using Snowpark to generate SQL queries with the DataFrame API.
- Writing and deploying Streamlit in Snowflake Apps.
- Writing and deploying Snowflake Native Apps, with the Snowflake Native App Framework.
- Integrating Snowflake with ChatGPT, external dashboards, data science and machine learning libraries.
- Second half of the course will be all about Snowflake client apps, Snowpark, Streamlit in Snowflake Apps and Native Apps.
What is NOT Included in This Course
- In-depth knowledge of Snowflake.
- In-depth data science, data analytics and machine learning.
- Programming in languages other than Python and SQL.
- Main focus will be on all sorts of applications in Python using Streamlit, to connect and deploy the code to Streamlit Cloud or Snowflake in all possible ways.
Real-Life Applications You Will Learn To Build
- Hierarchical Data Viewer, for CSV files and Snowflake tabular data, using JSON, graphs, animations, recursive queries.
- Hierarchical Metadata Viewer, for Snowflake object dependencies and data lineage.
- Entity-Relationship Diagram Viewer for Snowflake.
- Chatbot Agent with OpenAI’s ChatGPT, used as a SQL query generator for Snowflake Marketplace datasets.
- Dashboards for Snowflake data, with Vega-Lite, Altair and Plotly charts.
- Machine Learning scenarios, with Model Training and Predictions.
- Data enrichment of IP addresses using external free services.
- I sold tools similar to many of these to real-life clients and Snowflake partners!
Enroll today, to keep this course forever! Read more about here…
Also Available on Udemy (click for a Constant Low Price!)
Curriculum
- 5 Sections
- 52 Lessons
- Lifetime
Expand all sectionsCollapse all sections
- Introduction4
- Testing Local Streamlit Web Apps13
- 2.1Introduction and Section Summary
- 2.2Build a Simple Hierarchical Data Viewer in Python
- 2.3Convert the Hierarchical Data Viewer to a Streamlit Web App
- 2.4Hierarchical Data Charts in Streamlit with Plotly
- 2.5Streamlit Layout Components
- 2.6Add Hierarchical Formats and Animation
- 2.7Improve Original Data Viewer App and Make It More Generic
- 2.8Use Streamlit Input Controls
- 2.9Cache Data Between Page Reruns
- 2.10Save State Data Between Page Reruns
- 2.11Use Control Callbacks on Page Reruns
- 2.12Finalize the Hierarchical Data Viewer as a Streamlit Web App
- 2.13Test Your Knowledge10 Minutes0 Questions
- Sharing Streamlit Web Apps in Streamlit Cloud7
- 3.1Introduction and Section Summary
- 3.2Deploy Your Local Web App to Streamlit Cloud
- 3.3Use Data Caching with a Generated Session ID
- 3.4Make App Private and Protect Public App Access
- 3.5Data Analysis of Real-Estate Properties with a BI Streamlit App
- 3.6ML Object Detection with a CNN Data Science Streamlit App
- 3.7Test Your Knowledge10 Minutes0 Questions
- Connecting Streamlit Apps to Snowflake18
- 4.1Introduction and Section Summary
- 4.2Upload Data into a New Snowflake Account through the Web UI
- 4.3Connect to Snowflake with SnowSQL CLI
- 4.4Connect to Snowflake with the Connector for Python
- 4.5Connect to Snowflake with Snowpark for Python
- 4.6Build a Complex Query with the Python Client and Snowpark
- 4.7Build a Complex Query with the Snowpark DataFrame API
- 4.8Push Python Code as a Stored Procedure with Snowpark
- 4.9Connect to Snowflake with Streamlit Connector in Multi-Page App
- 4.10Connect the Hierarchical Data Viewer to Snowflake
- 4.11Enhance the Hierarchical Data Viewer with Recursive Queries
- 4.12Deploy the Connected Hierarchical Data Viewer to Streamlit Cloud
- 4.13Create a Hierarchical Metadata Viewer as a Streamlit Multi-Page App
- 4.14Create an Entity-Relationship Diagram Viewer with Streamlit
- 4.15Create a NLP Sentiment Analysis App with the IMDB Reviews
- 4.16Integrate Snowflake with ChatGPT
- 4.17Create a ChatGPT Agent for Your Web Pages
- 4.18Test Your Knowledge10 Minutes0 Questions
- Deploying Streamlit Apps to Snowflake14
- 5.1Introduction and Section Summary
- 5.2Create and Deploy a Streamlit in Snowflake App
- 5.3Deploy the Hierarchical Data Viewer in Snowflake as a Streamlit App
- 5.4Deploy the Hierarchical Metadata Viewer in Snowflake as a Streamlit App
- 5.5Create a Multi-Page Dashboard with Vega-Lite Charts as a Streamlit App
- 5.6Create a Multi-Page Dashboard with Altair Charts as a Streamlit App
- 5.7Train a Linear Regression ML Model and Predict with UDF
- 5.8Deploy the Hierarchical Metadata Viewer as a Snowflake Native App
- 5.9Deploy the Hierarchical Data Viewer as a Snowflake Native App
- 5.10Review the Snowflake Native App Framework
- 5.11Enrich IP Address Data with a Snowflake Native App
- 5.12Install and Run a Free Snowflake Native App from the Marketplace
- 5.13Test Your Knowledge10 Minutes0 Questions
- 5.14Congratulations, You Made It!
Requirements
- Basic knowledge of SQL and relational databases
- Basic knowledge of Python programming
- No prior knowledge of Streamlit or Snowflake is expected
- Basic knowledge of working with a version-control code repository such as GitHub
- No prior knowledge of data science, data analytics or machine learning is expected
Features
- Build, debug and deploy data-driven applications with Streamlit
- Deploy Streamlit web apps into Snowflake, as Streamlit in Snowflake Apps
- Share and deploy Streamlit web apps as Snowflake Native Apps
- Deploy Python code with Snowpark as Snowflake stored procedures and UDFs
- Connect to Snowflake from a Streamlit web application
- Build real-life applications with Streamlit and Snowflake
- Design and deploy to Snowflake data science, data analysis and ML apps with Streamlit
- Process and access hierarchical data and metadata in Snowflake
Target audiences
- Data Engineers looking to improve their programming skills with data-driven applications
- Data Scientists looking to learn fast prototyping with Streamlit
- Software Developers looking to learn a rapid data application development framework
- Snowflake Admins looking to master the new Streamlit App and Snowflake Native App frameworks
- Data Architects looking to learn about deploying Snowflake Native Apps with Streamlit
- Data Analysts looking to learn about using Streamlit to build instant dashboards
- Any other technical person looking to learn about using Streamlit to build Snowflake connected applications
- Machine Learning Engineers looking for data science projects in Snowflake