Snowpark has become an essential framework for modern data engineering, analytics, and machine learning workflows. This course equips learners with the skills to leverage Snowpark effectively and apply it across real-world data challenges.
The Ultimate Guide to Snowpark
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Harness Snowpark with Python for diverse workloads.
Develop robust data pipelines using Snowpark.
Deploy and operate machine learning models with Snowpark.
Skills you'll gain
- MLOps (Machine Learning Operations)
- Data Analysis
- Data Science
- Data Pipelines
- Cloud Computing
- Application Deployment
- Data Import/Export
- Data Processing
- Data Transformation
- Containerization
- Pandas (Python Package)
- Snowflake Schema
- Python Programming
- Large Language Modeling
- SQL
- Machine Learning
- Model Deployment
- Application Development
Details to know
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Recently updated!
January 2026
Assessments
8 assignments
Taught in English
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There are 8 modules in this course
In this section, we explore Snowpark's Python integration, scalability, and applications in data engineering and science.
What's included
2 videos1 reading1 assignment
In this section, we explore configuring the Snowpark environment, structuring projects, and using Python worksheets for efficient data application development with Snowpark.
What's included
1 video7 readings1 assignment
In this section, we explore data ingestion, transformation, and analysis using Snowpark. Key concepts include CSV, JSON, and Parquet loading, DataFrame operations, and pandas integration for efficient data workflows.
What's included
1 video6 readings1 assignment
In this section, we explore building resilient data pipelines with Snowpark, deploying efficient DataOps, and implementing logging and tracing for reliable data delivery.
What's included
1 video6 readings1 assignment
In this section, we explore data science workflows in Snowpark, focusing on data preparation, ML model training, and efficient processing in Data Cloud for scalable applications.
What's included
1 video5 readings1 assignment
In this section, we explore deploying ML models in Snowpark, managing model data with feature stores, and tracking metrics and versions in registries for scalable and reproducible workflows.
What's included
1 video5 readings1 assignment
In this section, we explore building, publishing, and managing native applications using Snowpark. Key concepts include framework components, marketplace distribution, and version control for efficient data workflows.
What's included
1 video4 readings1 assignment
In this section, we explore deploying containerized applications and large language models (LLMs) using Snowpark Container Services. It covers setup, job configuration, and efficient resource management for data-centric workflows.
What's included
1 video5 readings1 assignment
Instructor
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
More questions
Financial aid available,