Streamlit For Snowflake Masterclass 2024 Hands–On!

What you'll learn
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
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
Description
Why You Can Trust MeI was the only Snowflake technical expert from Canada selected for their Data Superhero program in 2021.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 LearnHow 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 AboutInput 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 AboutCreating 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 CourseIn-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 BuildHierarchical 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!
Overview
Section 1: Introduction
Lecture 1 Course Structure and Content
Lecture 2 Best Ways to Benefit from this Course
Lecture 3 All Slides and Resources
Lecture 4 Initial Requirements and Project Setup
Section 2: Testing Local Streamlit Web Apps
Lecture 5 Introduction and Section Summary
Lecture 6 Build a Simple Hierarchical Data Viewer in Python
Lecture 7 Convert the Hierarchical Data Viewer to a Streamlit Web App
Lecture 8 Hierarchical Data Charts in Streamlit with Plotly
Lecture 9 Streamlit Layout Components
Lecture 10 Add Hierarchical Formats and Animation
Lecture 11 Improve Original Data Viewer App and Make It More Generic
Lecture 12 Use Streamlit Input Controls
Lecture 13 Cache Data Between Page Reruns
Lecture 14 Save State Data Between Page Reruns
Lecture 15 Use Control Callbacks on Page Reruns
Lecture 16 Finalize the Hierarchical Data Viewer as a Streamlit Web App
Section 3: Sharing Streamlit Web Apps in Streamlit Cloud
Lecture 17 Introduction and Section Summary
Lecture 18 Deploy Your Local Web App to Streamlit Cloud
Lecture 19 Use Data Caching with a Generated Session ID
Lecture 20 Make App Private and Protect Public App Access
Lecture 21 Data Analysis of Real-Estate Properties with a BI Streamlit App
Lecture 22 ML Object Detection with a CNN Data Science Streamlit App
Section 4: Connecting Streamlit Apps to Snowflake
Lecture 23 Introduction and Section Summary
Lecture 24 Upload Data into a New Snowflake Account through the Web UI
Lecture 25 Connect to Snowflake with SnowSQL CLI
Lecture 26 Connect to Snowflake with the Connector for Python
Lecture 27 Connect to Snowflake with Snowpark for Python
Lecture 28 Build a Complex Query with the Python Client and Snowpark
Lecture 29 Build a Complex Query with the Snowpark DataFrame API
Lecture 30 Push Python Code as a Stored Procedure with Snowpark
Lecture 31 Connect to Snowflake with Streamlit Connector in Multi-Page App
Lecture 32 Connect the Hierarchical Data Viewer to Snowflake
Lecture 33 Enhance the Hierarchical Data Viewer with Recursive Queries
Lecture 34 Deploy the Connected Hierarchical Data Viewer to Streamlit Cloud
Lecture 35 Create a Hierarchical Metadata Viewer as a Streamlit Multi-Page App
Lecture 36 Create an Entity-Relationship Diagram Viewer with Streamlit
Lecture 37 Create a NLP Sentiment Analysis App with the IMDB Reviews
Lecture 38 Integrate Snowflake with ChatGPT
Section 5: Deploying Streamlit Apps to Snowflake
Lecture 39 Introduction and Section Summary
Lecture 40 Create and Deploy a Streamlit in Snowflake App
Lecture 41 Deploy the Hierarchical Data Viewer in Snowflake as a Streamlit App
Lecture 42 Deploy the Hierarchical Metadata Viewer in Snowflake as a Streamlit App
Lecture 43 Create a Multi-Page Dashboard with Vega-Lite Charts as a Streamlit App
Lecture 44 Create a Multi-Page Dashboard with Altair Charts as a Streamlit App
Lecture 45 Train a Linear Regression ML Model and Predict with UDF
Lecture 46 Deploy the Hierarchical Metadata Viewer as a Snowflake Native App
Lecture 47 Deploy the Hierarchical Data Viewer as a Snowflake Native App
Lecture 48 Review the Snowflake Native App Framework
Lecture 49 Enrich IP Address Data with a Snowflake Native App
Lecture 50 Install and Run a Free Snowflake Native App from the Marketplace
Lecture 51 Thank You and Goodbye
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
Homepage

https://www.udemy.com/course/streamlit-for-snowflake/