BEGINNER • SQL Fundamentals
ETL Checkpoint #18
This lesson focuses on reduce pipeline latency for a user behavior tracking environment. You will use: python etl_script.py | CREATE TABLE events (id SERIAL PRIMARY KEY) | python -m venv venv. The content is designed for practical data engineering execution.
Code Example
@task
def extract():
return fetch_from_api("user behavior tracking")
@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
- python etl_script.py
- CREATE TABLE events (id SERIAL PRIMARY KEY)
- python -m venv venv
Lab Steps
- Prepare environment with: python etl_script.py
- 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.