Introduction (10 mins):

Start off the lesson by introducing the concept of data engineering. Explain what data engineering is, how it differs from data analytics/science, and how it is used to make data-driven decisions. Discuss the major components of data engineering, including data workflow tools such as Apache Airflow and dbt. Show the students examples of data analysis pipelines built on Airflow and dbt (such as the example here:https://www.youtube.com/watch?v=0SO77eprrtE), and have them think about what a data engineering workflow can do.

Lesson Outline (25 mins):

Now it’s time to dive deeper into the specifics of data engineering with Airflow and dbt. Start by demonstrating how to set up a data engineering workflow using Airflow. Show the students examples of the tasks they can set up and how they can be connected and managed.

Next, discuss dbt and how it can be used in conjunction with Airflow to analyze data. Demonstrate how to use dbt to run queries and create visualizations. Explain how dbt also makes it easier to manage changes to the data pipeline, as well as the data itself.

Finally, discuss how data engineering pipelines with Airflow and dbt can be used to automate processes and make data processes more efficient. Demonstrate how the workflow can be triggered on a regular basis and how the results can be stored and accessed when needed.

Questions (10 mins):

Now that you’ve gone over the basics of data engineering with Airflow and dbt, it’s time to ask the students to answer a few questions. You can use questions like these to assess their understanding:

1. What is the purpose of data engineering?
2. How does Apache Airflow differ from dbt?
3. What are the benefits of using Airflow and dbt together?
4. How can a data engineering workflow help automate tasks?

Assessment (10 mins):

Assess the student’s understanding by having them complete a worksheet. This worksheet can include questions related to the topics discussed and demonstrate their ability to configure and implement an Airflow and dbt-powered data engineering pipeline (link to example worksheet here: https://www.dbt.io/exercise-02/).

Differentiation (5 mins):

For students who may be struggling to understand the material, provide tools such as extra practice worksheets, tutorial videos, and other resources (link to useful videos here: https://www.youtube.com/watch?v=FeOc01tgX_c). Additionally, have students work together in groups as a way to help each other better understand the concepts.

Plenary (5 mins):

At the end of the lesson, review the major points from the lesson and provide a recap. Remind students of the benefits of using Airflow and dbt for data engineering, such as automating tasks and making the data pipeline more efficient. Provide additional resources for further exploration and ask for questions.