BEGINNER • SQL Fundamentals
ETL Checkpoint #3
This lesson focuses on increase data discoverability for a financial reporting 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
@task
def extract():
return fetch_from_api("financial reporting")
@task
def transform(data):
return clean_and_validate(data)
@flow
def etl_pipeline():
raw = extract()
transformed = transform(raw)
load_to_warehouse(transformed)
# Run: prefect deploy flow.pyCommands & 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.