Overview
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.
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.
Basic Python programming skills, Basic knowledge of SQL, Some familiarity with the Snowflake platform
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
Cristian Scutaru
In only half a year on Udemy, most of my video courses became best-sellers and highest rated.
World-class expert in Snowflake Data Cloud, former Snowflake "Data Superhero". SnowPro Certification SME (Subject Matter Expert), with five SnowPro exams (out of all six), all passed from the first attempt.
Over 40 proctored certification exams passed in the last 3-4 years alone, all from the first attempt. Dozens on certifications in AWS/Azure/GCP, in Data Science and Machine Learning.
Over three decades in the software industry, as a hands-on data and solutions architect, technical manager and team lead, software and data engineer. Successful entrepreneur and independent consultant. Former Microsoft employee (there is still code of mine in Microsoft SQL Server and Microsoft Windows).