BEGINNER • SQL Fundamentals
Data Pipeline for fraud detection pipeline #21
This lesson focuses on strengthen data governance for a fraud detection pipeline environment. You will use: INSERT INTO logs VALUES (...) | pip install pandas sqlalchemy | SELECT * FROM users LIMIT 10. The content is designed for practical data engineering execution.
Code Example
# dbt model: fact_fraud_detection_pipeline
{{ config(materialized='incremental') }}
SELECT
user_id,
event_date,
COUNT(*) as event_count
FROM {{ ref('staging_events') }}
{% if is_incremental() %}
WHERE event_date > (SELECT MAX(event_date) FROM {{ this }})
{% endif %}
GROUP BY 1, 2
-- Run: pip install pandas sqlalchemyCommands & References
- INSERT INTO logs VALUES (...)
- pip install pandas sqlalchemy
- SELECT * FROM users LIMIT 10
Lab Steps
- Prepare environment with: INSERT INTO logs VALUES (...)
- Design or modify the data pipeline for the scenario.
- Validate data quality and document lineage.
- Propose one optimization for production.
Exercises
- Add one data quality check.
- Implement one incremental loading pattern.
- Write a rollback procedure for this pipeline.