BEGINNER • SQL Fundamentals
Warehouse Lab for financial reporting #19
This lesson focuses on increase data discoverability for a financial reporting environment. You will use: SELECT * FROM users LIMIT 10 | INSERT INTO logs VALUES (...) | pip install pandas sqlalchemy. The content is designed for practical data engineering execution.
Code Example
-- Data quality check
SELECT
COUNT(*) as total_rows,
COUNT(DISTINCT id) as unique_ids
FROM fact_financial_reporting
WHERE created_at >= CURRENT_DATE - 1
-- Objective: increase data discoverabilityCommands & References
- SELECT * FROM users LIMIT 10
- INSERT INTO logs VALUES (...)
- pip install pandas sqlalchemy
Lab Steps
- Prepare environment with: SELECT * FROM users LIMIT 10
- 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.