Integration with dbt and Airflow in Snowflake - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is dbt and how does it help with Snowflake?

dbt (data build tool) helps transform raw data in Snowflake by writing simple SQL models. It organizes and tests data transformations, making data easier to manage and understand.

beginner

What role does Airflow play when working with dbt and Snowflake?

Airflow schedules and runs dbt jobs automatically. It helps manage workflows by running tasks in order, like refreshing data models in Snowflake at set times.

intermediate

How do you connect dbt to Snowflake?

You configure dbt with Snowflake credentials like account, user, password, warehouse, database, and schema in the dbt profile file. This lets dbt run SQL commands directly on Snowflake.

intermediate

What is a common way to run dbt models in Airflow?

Use Airflow's BashOperator or a custom operator to run dbt commands like 'dbt run' or 'dbt test'. This runs dbt models as part of an automated workflow.

intermediate

Why is it important to test dbt models in an Airflow workflow?

Testing ensures data quality and catches errors early. Running tests in Airflow helps keep data reliable by automatically checking models after they run.

What does dbt primarily do with Snowflake data?

A Visualizes data

B Transforms raw data using SQL models

C Schedules workflows

D Stores data in tables

dbt transforms raw data by running SQL models inside Snowflake.

Which tool is best for scheduling dbt jobs automatically?

A Airflow

B Snowflake UI

C Excel

D GitHub

Airflow is designed to schedule and manage workflows like dbt jobs.

What information is NOT needed to connect dbt to Snowflake?

A Snowflake account name

B User credentials

C Warehouse name

D Airflow DAG ID

Airflow DAG ID is unrelated to dbt's Snowflake connection.

How can Airflow run dbt commands?

A By writing SQL in Airflow

B Directly inside Snowflake UI

C Using BashOperator to run shell commands

D Using Excel macros

Airflow's BashOperator can run shell commands like 'dbt run'.

Why include dbt tests in Airflow workflows?

A To improve data quality automatically

B To slow down the workflow

C To delete old data

D To create dashboards

Running tests helps catch data issues early and keeps data reliable.

Explain how dbt and Airflow work together to manage data in Snowflake.

Think about how each tool's role fits in a data pipeline.

Describe the steps to set up a workflow that runs dbt models on Snowflake using Airflow.

Consider configuration, automation, and testing.