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Introduction to Modern Data Engineering with Snowflake

Snowflake Native Applications

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Introduction to Modern Data Engineering with Snowflake

SkillTag.level.intermediateSkillTag.label

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Discover how the explosion of data gave rise to data engineering, and how Snowflake reshapes the modern approach. You'll frame your work around the Ingestion-Transformation-Delivery (ITD) framework, prepare your development environment, and build your first simple pipeline in Snowflake.

Exercise 1: The explosion of data and the demand for insights Exercise 2: What we'll cover in this course Exercise 3: Modern data engineering with Snowflake Exercise 4: You've probably done some data engineering in the past Exercise 5: How to successfully complete the course Exercise 6: Preparing your development environment Exercise 7: Build a really simple data pipeline in Snowflake Exercise 8: Chapter 1 review - Question 1 Exercise 9: Chapter 1 review - Question 2 Exercise 10: Chapter 1 review - Question 3 Exercise 11: Chapter 1 review - Question 4 Exercise 12: Chapter 1 review - Question 5

Ingest data into Snowflake at scale using a range of powerful techniques. You'll load data through the Snowflake Marketplace, the Snowsight web interface, the Snowflake CLI, and the COPY INTO command, pull from external systems with native connectors, and tune virtual warehouses for efficient batch ingestion.

Exercise 1: What is data ingestion?Exercise 2: Batch ingestion with Snowflake Exercise 3: Loading data from Snowflake Marketplace Exercise 4: Loading data using Snowflake's web interface Exercise 5: Optimize compute resources for efficient batch ingestion Exercise 6: Loading data using Snowflake CLI Exercise 7: Loading data using the COPY INTO command Exercise 8: Scenario briefing and account setup Exercise 9: Ingesting data from other data systems using connectors Exercise 10: Recap and best practices for batch ingestion Exercise 11: Additional resources on batch ingestion with Snowflake Exercise 12: Troubleshooting errors encountered in Module 2 Exercise 13: Chapter 2 review - Question 1 Exercise 14: Chapter 2 review - Question 2 Exercise 15: Chapter 2 review - Question 3 Exercise 16: Chapter 2 review - Question 4 Exercise 17: Chapter 2 review - Question 5 Exercise 18: Chapter 2 review - Question 6 Exercise 19: Chapter 2 review - Question 7

Transform raw data into analysis-ready data using SQL and Snowpark for Python. You'll create and apply user-defined functions, stored procedures, streams, and Dynamic Tables, and run transformations from inside Visual Studio Code using Snowflake's official extension.

Exercise 1: What are data transformations?Exercise 2: Data transformations with SQL Exercise 3: Data transformations with Snowpark Exercise 4: Computations with user-defined functions Exercise 5: Efficient transformations with streams Exercise 6: Complex procedural logic with stored procedures Exercise 7: Automatic transformations with Dynamic Tables Exercise 8: Data transformations in Visual Studio Code (Optional)Exercise 9: Recap and best practices for data transformations Exercise 10: Additional resources on data transformations with Snowflake Exercise 11: Chapter 3 review - Question 1 Exercise 12: Chapter 3 review - Question 2 Exercise 13: Chapter 3 review - Question 3 Exercise 14: Chapter 3 review - Question 4 Exercise 15: Chapter 3 review - Question 5

Turn clean, transformed data into data products that deliver value. You'll share datasets through the Snowflake Marketplace, build and share insights with a Streamlit in Snowflake application, and deploy a Snowflake Native Application using the Snowflake CLI.

Exercise 1: Delivery of data products Exercise 2: Data sharing on Snowflake Marketplace Exercise 3: Streamlit in Snowflake Applications Exercise 4: Snowflake Native Applications

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Exercise 5: Recap and best practices for data product delivery Exercise 6: Additional resources on data product delivery with Snowflake Exercise 7: Chapter 4 review - Question 1 Exercise 8: Chapter 4 review - Question 2 Exercise 9: Chapter 4 review - Question 3 Exercise 10: Chapter 4 review - Question 4 Exercise 11: Chapter 4 review - Question 5

Bring your pipelines to life with orchestration. You'll automate pipeline steps using user-managed and serverless tasks, link tasks into a task graph (DAG), and execute individual tasks and entire DAGs to run continuous, automated data pipelines.

Exercise 1: What is orchestration?Exercise 2: Automation with tasks Exercise 3: Orchestration with DAGs Exercise 4: Recap and best practices for pipeline orchestration Exercise 5: Conclusion Exercise 6: Additional resources on orchestration with Snowflake Exercise 7: Troubleshooting errors encountered in Module 5 Exercise 8: Beyond the course Exercise 9: Chapter 5 review - Question 1 Exercise 10: Chapter 5 review - Question 2 Exercise 11: Chapter 5 review - Question 3 Exercise 12: Chapter 5 review - Question 4 Exercise 13: Chapter 5 review - Question 5