BEGINNER • SQL Fundamentals
Warehouse Lab for e-commerce analytics #9
This lesson focuses on improve data quality for a e-commerce analytics 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
-- Data quality check
SELECT
COUNT(*) as total_rows,
COUNT(DISTINCT id) as unique_ids
FROM fact_e-commerce_analytics
WHERE created_at >= CURRENT_DATE - 1
-- Objective: improve data qualityCommands & 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.