BEGINNER • SQL Fundamentals
Data Engineering Playbook #5
This lesson focuses on strengthen data governance for a fraud detection pipeline environment. You will use: pip install pandas sqlalchemy | SELECT * FROM users LIMIT 10 | INSERT INTO logs VALUES (...). The content is designed for practical data engineering execution.
Code Example
-- Data pipeline for fraud detection pipeline
-- Objective: strengthen data governance
CREATE TABLE IF NOT EXISTS staging_events (
id BIGINT,
event_type VARCHAR(50),
created_at TIMESTAMP
);
INSERT INTO staging_events
SELECT id, event_type, created_at
FROM raw_events
WHERE created_at >= CURRENT_DATE - INTERVAL '1 day';
-- Verify: pip install pandas sqlalchemyCommands & References
- pip install pandas sqlalchemy
- SELECT * FROM users LIMIT 10
- INSERT INTO logs VALUES (...)
Lab Steps
- Prepare environment with: pip install pandas sqlalchemy
- 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.