Cross-team model sharing in dbt - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Cross-team Model Dependencies in dbt

In dbt, when multiple teams share models, how can one team ensure their models correctly depend on models from another team?

A By referencing the other team's models using the full project and schema name in the ref function, like ref('project_name', 'model_name')

B By copying the other team's models into their own project to avoid dependencies

C By using the ref function with only the model name, assuming dbt will find it automatically across projects

D By manually writing SQL joins without using ref to avoid cross-project references

💡 Hint

Think about how dbt resolves model references across different projects.

dbt allows referencing models from other projects by specifying the project name in the ref function. This ensures correct dependency tracking and compilation.

❓ data_output

intermediate

Output of Cross-project Model Reference in dbt

Given two dbt projects: team_a and team_b. Team B's model sales_summary references Team A's model raw_sales using ref('team_a', 'raw_sales'). What will be the compiled SQL schema and table name for raw_sales in Team B's compiled SQL?

A The compiled SQL will reference <code>public.raw_sales</code> by default.

B The compiled SQL will reference <code>team_b.raw_sales</code> ignoring the project name in ref.

C The compiled SQL will fail to compile due to cross-project reference errors.

D The compiled SQL will reference <code>team_a.raw_sales</code> with the schema and table name as defined in Team A's project configuration.

💡 Hint

Consider how dbt compiles references across projects.

When referencing models from another project, dbt uses that project's schema and table naming conventions in the compiled SQL.

🔧 Debug

advanced

Debugging Cross-team Model Reference Failure

Team B tries to run a model referencing Team A's model using ref('team_a', 'customer_data') but gets an error: Compilation Error: model 'team_a.customer_data' not found. What is the most likely cause?

A Team B's dbt version is incompatible with Team A's dbt version.

B The model name <code>customer_data</code> is misspelled in Team B's ref call.

C Team A's project is not included as a dependency in Team B's <code>packages.yml</code> file.

D Team B's model is missing a <code>schema.yml</code> file.

💡 Hint

Think about how dbt knows about other projects' models.

For cross-project references to work, the referenced project must be added as a dependency in packages.yml. Without this, dbt cannot find the model.

❓ visualization

advanced

Visualizing Cross-team Model Dependencies

You have two dbt projects: finance and marketing. Finance models depend on marketing models. Which visualization best represents the dependency graph showing cross-team model sharing?

A A pie chart showing the percentage of models per team.

B A directed graph where nodes represent models, edges show dependencies, and edges crossing project boundaries are highlighted in a different color.

C A bar chart showing the number of models per project without dependencies.

D A scatter plot of model run times versus model sizes.

💡 Hint

Think about how to show relationships and dependencies clearly.

A directed graph with highlighted cross-project edges clearly shows which models depend on others across teams.

🚀 Application

expert

Implementing Cross-team Model Sharing with Version Control

Your organization wants to share dbt models across teams with strict version control. Which approach best supports this goal?

A Publish shared models as a versioned dbt package in a private repository, and have consuming teams specify exact package versions in their <code>packages.yml</code>.

B Disable dbt's dependency management and manually manage SQL files across teams.

C Use a shared database schema for all teams without versioning, relying on communication to manage changes.

D Copy shared models into each team's project and update manually when changes occur.

💡 Hint

Consider how to manage versions and dependencies cleanly in dbt.

Using versioned dbt packages in private repositories allows teams to depend on specific versions, ensuring stability and controlled updates.