ETL Pipelines Cheat Sheet
Covers extract-transform-load pipeline design, Airflow DAG orchestration, and best practices for idempotent, reliable, and incrementally loading data pipelines.
2 PagesIntermediateMar 15, 2026
Orchestrating with Airflow
Define a daily ETL DAG with explicit task dependencies.
python
from airflow import DAGfrom airflow.operators.python import PythonOperatorfrom datetime import datetimedef extract(): ...def transform(): ...def load(): ...with DAG( dag_id="daily_sales_etl", schedule="0 2 * * *", # 2am daily start_date=datetime(2024, 1, 1), catchup=False,) as dag: extract_task = PythonOperator(task_id="extract", python_callable=extract) transform_task = PythonOperator(task_id="transform", python_callable=transform) load_task = PythonOperator(task_id="load", python_callable=load) extract_task >> transform_task >> load_task
Extract-Transform-Load with pandas
A minimal ETL step implemented directly in pandas.
python
import pandas as pd# Extractdf = pd.read_csv("raw_sales.csv")# Transformdf["order_date"] = pd.to_datetime(df["order_date"])df = df.dropna(subset=["customer_id"])df["amount"] = df["amount"].clip(lower=0)df["region"] = df["region"].str.upper().str.strip()# Loaddf.to_sql("sales_clean", con=engine, if_exists="append", index=False)
ETL vs. ELT Concepts
Core terminology in pipeline design.
- Extract- Pull raw data from source systems (databases, APIs, files, event streams)
- Transform- Clean, validate, deduplicate, and reshape data into the target schema
- Load- Write the transformed data into the destination warehouse or data mart
- ETL vs ELT- ETL transforms before loading; ELT loads raw data first and transforms inside the warehouse (e.g. dbt)
- Idempotency- Re-running a pipeline with the same input should produce the same output, with no duplicates
- Incremental load- Only process new/changed records (via timestamp or CDC) instead of full reloads
Best Practices
Habits that keep pipelines reliable in production.
- Schema validation- Validate incoming data against an expected schema before it reaches downstream tables
- Data quality checks- Assert row counts, null rates, and value ranges at each pipeline stage
- Retry and alerting- Automatically retry transient failures and alert on-call when a pipeline fails
- Orchestration- Use a scheduler like Airflow, Dagster, or Prefect to manage dependencies between tasks
- Backfilling- Ability to reprocess historical date ranges when logic changes or bugs are fixed
Pro Tip
Design every load step to be idempotent, such as upserting on a natural key or overwriting by partition - pipelines will eventually be re-run after a failure, and non-idempotent loads silently create duplicate rows.
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