Use it when you want to begin dbt advanced patterns work without writing the first draft from scratch.
dbt Macros and Reusability AI Prompt
Write reusable dbt macros for common transformation patterns in this project. Repetitive patterns identified: {{patterns}} (e.g. currency conversion, fiscal calendar, event dedu... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.
Write reusable dbt macros for common transformation patterns in this project.
Repetitive patterns identified: {{patterns}} (e.g. currency conversion, fiscal calendar, event deduplication)
Warehouse: {{warehouse}}
1. Basic macro structure:
{% macro cents_to_dollars(column_name, precision=2) %}
ROUND({{ column_name }} / 100.0, {{ precision }})
{% endmacro %}
Usage in a model:
SELECT {{ cents_to_dollars('amount_cents') }} AS amount_dollars
2. Deduplication macro (common pattern):
{% macro deduplicate(relation, partition_by, order_by) %}
SELECT *
FROM (
SELECT
*,
ROW_NUMBER() OVER (
PARTITION BY {{ partition_by }}
ORDER BY {{ order_by }} DESC
) AS _row_number
FROM {{ relation }}
)
WHERE _row_number = 1
{% endmacro %}
Usage:
{{ deduplicate(ref('stg_orders'), 'order_id', 'updated_at') }}
3. Date spine macro (dbt-utils built-in):
{{ dbt_utils.date_spine(
datepart='day',
start_date=cast('2020-01-01' as date),
end_date=cast(now() as date)
) }}
4. Generate surrogate key:
{{ dbt_utils.generate_surrogate_key(['order_id', 'line_item_id']) }}
- MD5 hash of concatenated key columns
- Use as primary key for fact tables without a natural unique key
5. Star schema helper macros:
- Union multiple tables of the same schema:
{{ dbt_utils.union_relations(relations=[ref('orders_us'), ref('orders_eu')]) }}
- Pivot rows to columns:
{{ dbt_utils.pivot('metric_name', ['revenue', 'cost', 'profit'], agg='SUM', then_value='metric_value') }}
6. Macro testing:
- Write a simple model that uses the macro and add generic tests on its output
- Add a CI step: dbt compile → verify compiled SQL for macros is correct
Return: macro implementations for the identified patterns, usage examples, and testing approach.When to use this prompt
Use it when you want a more consistent structure for AI output across projects or datasets.
Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.
Use it when you want a clear next step into adjacent prompts in dbt Advanced Patterns or the wider Analytics Engineer (dbt) library.
What the AI should return
The AI should return a structured result that covers the main requested outputs, such as Basic macro structure:, Deduplication macro (common pattern):, Date spine macro (dbt-utils built-in):. The final answer should stay clear, actionable, and easy to review inside a dbt advanced patterns workflow for analytics engineer (dbt) work.
How to use this prompt
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in dbt Advanced Patterns.
Frequently asked questions
What does the dbt Macros and Reusability prompt do?+
It gives you a structured dbt advanced patterns starting point for analytics engineer (dbt) work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for analytics engineer (dbt) workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
dbt Macros and Reusability is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are dbt CI/CD Pipeline, dbt for Machine Learning Features, dbt Metrics Layer.