What You Need to Know Before
You Start

Starts 22 June 2025 13:10

Ends 22 June 2025

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Snowflake Data Engineering

Master Snowflake data engineering through hands-on exercises covering data ingestion, transformations, pipeline orchestration, DevOps practices, and observability to build practical skills for real-world projects.
Snowflake via Coursera

Snowflake

2041 Courses


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Paid Course

Optional upgrade avallable

Overview

Are you looking to build a career in data engineering? Or want to get hands-on experience with Snowflake for your next project?

Whether you’re a student, early career professional, or an experienced data professional, this program will enable you to acquire the practical skills you’ll need to be competitive in the job market to land your next job, or to grow your existing career as a data professional. Created and delivered by Snowflake’s own developer advocates, this program emphasizes hands-on learning with lots of in-product exercises to give you the confidence you’ll need to tackle real-life, on-the-job projects.

You will learn:

How to use Snowflake to ingest data at scale How to perform data transformations with SQL and Python How to deliver data products to visualize insights How to orchestrate data pipelines How to implement DevOps best practices for data pipelines How to implement the observability of data pipelines

Syllabus

  • Introduction to Snowflake
  • Overview of Snowflake Architecture
    Cloud Data Warehousing Concepts
    Setting Up Your Snowflake Environment
  • Ingesting Data at Scale
  • Data Loading Techniques
    Using Snowpipe for Continuous Data Ingestion
    Handling Semi-Structured and Structured Data
  • Data Transformations with SQL and Python
  • SQL Transformations in Snowflake
    Integrating Python for Data Manipulation
    Utilizing Snowflake's Ecosystem for Advanced Transformations
  • Delivering Data Products
  • Data Warehousing Best Practices
    Creating Views and Tables for Data Products
    Building Dashboards for Data Visualization
  • Orchestrating Data Pipelines
  • Introduction to Data Pipelines
    Utilizing Snowflake Tasks and Streams
    Scheduling and Automating Workflows
  • Implementing DevOps Best Practices
  • Version Control and CI/CD for Data Pipelines
    Managing Environments and Deployments
    Ensuring Data Quality with Testing Methods
  • Observability of Data Pipelines
  • Monitoring and Logging in Snowflake
    Implementing Alerts and Notifications
    Performance Optimization and Tuning
  • Hands-On Labs and Projects
  • Real-world Scenarios and Challenges
    Collaborative Projects
    Capstone Project: Building a Comprehensive Data Engineering Solution
  • Course Wrap-Up
  • Review Key Concepts
    Future Trends in Data Engineering and Snowflake
    Career Pathways and Resources

Taught by

Snowflake Northstar


Subjects

Business