Introduction to Modern Data Engineering with Snowflake

Instructor: Snowflake Northstar

Sponsored by Louisiana Workforce Commission

2,967 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

4.7

(56 reviews)

Intermediate level

Recommended experience

6 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

4.7

(56 reviews)

Intermediate level

Recommended experience

6 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

How to use Snowflake to ingest data at scale, perform data transformations against data, deliver data products, and orchestrate data pipelines

Details to know

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Assessments

5 assignments

Taught in English

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There are 5 modules in this course

This is a technical, hands-on course that teaches learners how to build modern and continuous data pipelines with Snowflake. It focuses specifically on the most practical Snowflake concepts and tools to get learners up and running quickly with building data pipelines.

Learners start by learning about the "Ingestion-Transformation-Delivery" framework for modern data engineering, and dive deeper into each component of the framework by learning how to: - Ingest data into Snowflake at scale using a variety of powerful techniques - Perform data transformations with SQL or Snowpark - Extend data transformations with user-defined functions, stored procedures, streams, and Snowflake Dynamic Tables - Deliver valuable data products through Snowflake Marketplace, Streamlit in Snowflake, and Snowflake Native Applications - Orchestrate pipelines using tasks and DAGs Throughout the course, learners follow along with the instructor using a combination of Snowflake, Visual Studio Code, GitHub, and the command line. The course is supplemented with readings containing plenty of resources to level up the learner's understanding of specific concepts. Learners come away understanding how to build end-to-end, continuous data pipelines with Snowflake.

Learners understand how the explosion of data in recent years has led to an increased demand for extracting insights from that data, giving rise to data engineering. They'll understand previous data engineering approaches, modern data engineering approaches using Snowflake, and contextualize data engineering with the Ingestion-Transformation-Delivery ("ITD") framework. They also prepare their development environment and build a simple data pipeline with Snowflake.

What's included

6 videos2 readings1 assignment

6 videosTotal 27 minutes

The explosion of data and the demand for insights 3 minutesPreview module
What we’ll cover in this course2 minutes
Modern data engineering with Snowflake4 minutes
You’ve probably done some data engineering in the past3 minutes
Preparing your development environment4 minutes
Build a really simple data pipeline in Snowflake9 minutes

2 readingsTotal 20 minutes

How to successfully complete the course10 minutes
Have Questions? Join the Q+A Forum for this course10 minutes

1 assignmentTotal 15 minutes

Module 1 Assessment (Knowledge Check)15 minutes

Learners learn how to ingest data into Snowflake at scale using various, powerful techniques. They specifically load data into Snowflake using the Snowflake Marketplace, the Snowflake web interface (Snowsight), the Snowflake CLI, and the powerful COPY INTO SQL command. Learners also understand how to ingest data from external systems using Snowflake native connectors, and how to optimize data ingestion by fully utilizing a virtual warehouse.

What's included

9 videos3 readings1 assignment

9 videosTotal 50 minutes

What is data ingestion?3 minutesPreview module
Batch ingestion with Snowflake4 minutes
Loading data from Snowflake Marketplace6 minutes
Loading data using Snowflake’s web interface7 minutes
Optimize compute resources for efficient batch ingestion6 minutes
Loading data using Snowflake CLI7 minutes
Loading data using the COPY INTO command8 minutes
Ingesting data from other data systems using connectors2 minutes
Recap and best practices for batch ingestion3 minutes

3 readingsTotal 30 minutes

Scenario briefing and account setup10 minutes
Additional resources on batch ingestion with Snowflake10 minutes
Troubleshooting errors encountered in Module 210 minutes

1 assignmentTotal 15 minutes

Module 2 Assessment (Knowledge Check)15 minutes

Learners perform data transformations using SQL or Snowpark for Python. Learners also understand that they can use Snowpark to perform data transformations with Java and Scala. They also extend their knowledge of data transformations by learning about, creating, and using user-defined functions (UDFs), stored procedures, streams, and Snowflake Dynamic Tables. They also learn how to perform data transformations using these tools outside of Snowflake, specifically in Visual Studio Code using Snowflake's official extension.

What's included

9 videos1 reading1 assignment

9 videosTotal 49 minutes

What are data transformations?2 minutesPreview module
Data transformations with SQL5 minutes
Data transformations with Snowpark7 minutes
Computations with user-defined functions7 minutes
Efficient transformations with streams6 minutes
Complex procedural logic with stored procedures6 minutes
Automatic transformations with Dynamic Tables8 minutes
Data transformations in Visual Studio Code (Optional)4 minutes
Recap and best practices for data transformations1 minute

1 readingTotal 10 minutes

Additional resources on data transformations with Snowflake10 minutes

1 assignmentTotal 15 minutes

Module 3 Assessment (Knowledge Check)15 minutes

Learners understand what orchestration means, and how to add automation to data pipelines using tasks. They specifically learn about user-managed and serverless tasks, and create tasks to automate calls to stored procedures. They also create and link tasks together to form a task graph, or DAG, and execute individual tasks and entire DAGs.