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Airflow DAG Templates

$29

Production-ready Airflow DAG templates for modern data pipelines with error handling and monitoring.

📁 29 files🏷 v2.0.0 (updated 2026-07-01)

Production-ready

✓ Instant download✓ Lifetime updates✓ MIT licensed✓ MIT license✓ Secure checkout (Stripe)

📋 What's Inside 29 files

✓ README.md—
✓ manifest.json—
✓ LICENSE—
✓ dags/etl_pipeline_dag.py—
✓ dags/data_quality_dag.py—
✓ dags/dbt_orchestration_dag.py—
✓ dags/api_ingestion_dag.py—
✓ dags/cdc_streaming_dag.py—
✓ dags/backfill_dag.py—
✓ dags/file_sensor_dag.py—
✓ dags/data_warehouse_load_dag.py—
✓ dags/ml_pipeline_dag.py—
✓ dags/database_replication_dag.py—
✓ dags/sla_monitoring_dag.py—
✓ dags/cleanup_dag.py—

📁 File Structure 29 files

airflow-dag-templates/
├── README.md
├── manifest.json
├── LICENSE
├── dags/
│ ├── etl_pipeline_dag.py
│ ├── data_quality_dag.py
│ ├── dbt_orchestration_dag.py
│ ├── api_ingestion_dag.py
│ ├── cdc_streaming_dag.py
│ ├── backfill_dag.py
│ ├── file_sensor_dag.py
│ ├── data_warehouse_load_dag.py
│ ├── ml_pipeline_dag.py
│ ├── database_replication_dag.py
│ ├── sla_monitoring_dag.py
│ ├── cleanup_dag.py
│ ├── cross_dag_dependency_dag.py
│ ├── dynamic_task_mapping_dag.py
├── plugins/
│ ├── operators/
│ │ ├── spark_submit_operator.py
│ │ ├── data_quality_operator.py
│ │ ├── databricks_notebook_operator.py
│ │ ├── delta_table_sensor.py
│ ├── hooks/
│ │ ├── slack_webhook_hook.py
│ │ ├── teams_webhook_hook.py
│ ├── sensors/
│ │ ├── s3_key_sensor_extended.py
├── configs/
│ ├── connections.yaml
│ ├── variables.yaml
│ ├── dag_factory_config.yaml
├── tests/
│ ├── test_dags.py
├── guides/
│ ├── airflow-best-practices.md

📖 Documentation Preview README excerpt

Airflow DAG Templates

Production-ready Apache Airflow DAG templates for modern data pipelines.

> Skip the boilerplate. Start with 14 battle-tested DAGs covering ETL, data quality, ML pipelines, warehouse loading, CDC streaming, database replication, SLA monitoring, dynamic task mapping, and more.

---

What You Get

Category	Count	Highlights
DAG templates	14	ETL, ML, CDC, dbt, warehouse, replication, SLA, cleanup
Custom operators	4	Spark submit, data quality, Databricks notebook, Delta sensor
Custom hooks	2	Slack webhook, Microsoft Teams webhook
Custom sensors	1	Extended S3 key sensor with size/age checks
Config files	3	Connections, variables, DAG factory config
Test suite	1	DAG integrity, plugin validation, config checks
Best practices guide	1	450+ lines covering TaskFlow, dynamic mapping, SLAs

Total: 29 files, 7,000+ lines of production-quality code and documentation.

---

File Tree


airflow-dag-templates/
├── README.md                           # This file
├── manifest.json                       # Product manifest
├── LICENSE                             # MIT License
│
├── dags/                               # 14 production DAG templates
│   ├── etl_pipeline_dag.py             # Full ETL: extract → transform → quality → load
│   ├── data_quality_dag.py             # Data quality checks with Slack alerts
│   ├── dbt_orchestration_dag.py        # dbt run → test → docs generation
│   ├── api_ingestion_dag.py            # REST API → S3 → Spark processing
│   ├── cdc_streaming_dag.py            # Debezium → Kafka → Delta Lake CDC
│   ├── backfill_dag.py                 # Parameterized historical backfill
│   ├── file_sensor_dag.py              # S3 file watching with format routing
│   ├── data_warehouse_load_dag.py      # Multi-target warehouse loading (Snowflake/Redshift)
│   ├── ml_pipeline_dag.py              # ML training with evaluation gates
│   ├── database_replication_dag.py     # Full & incremental database replication
│   ├── sla_monitoring_dag.py           # SLA checks with tiered alerting
│   ├── cleanup_dag.py                  # Table cleanup, temp files, retention policies
│   ├── cross_dag_dependency_dag.py     # ExternalTaskSensor & TriggerDagRun patterns
│   └── dynamic_task_mapping_dag.py     # Airflow 2.3+ dynamic task mapping
│
├── plugins/                            # Custom Airflow plugins
│   ├── operators/
│   │   ├── spark_submit_operator.py    # Databricks Spark job submission
│   │   ├── data_quality_operator.py    # Configurable data quality checks
│   │   ├── databricks_notebook_operator.py  # Databricks notebook execution
│   │   └── delta_table_sensor.py       # Delta Lake table freshness sensor
│   ├── hooks/
│   │   ├── slack_webhook_hook.py       # Slack webhook notifications
│   │   └── teams_webhook_hook.py       # Microsoft Teams webhook notifications
│   └── sensors/
│       └── s3_key_sensor_extended.py   # S3 sensor with size/age validation
│

... preview truncated, see full README in product download.

`📄 Code Sample .py preview`

dags/etl_pipeline_dag.py"""
ETL Pipeline DAG — Airflow DAG Templates
===========================================
Full ETL pipeline: sensor → extract → transform → quality check → load → notify.
Includes retries, SLA monitoring, and Slack alerting on failure.

By Datanest Digital — https://datanest.dev
"""

from __future__ import annotations

from datetime import datetime, timedelta
from typing import Any, Dict

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.empty import EmptyOperator
from airflow.providers.amazon.aws.sensors.s3 import S3KeySensor
from airflow.utils.task_group import TaskGroup

# ---------------------------------------------------------------------------
# DAG Configuration — CUSTOMISE for your pipeline
# ---------------------------------------------------------------------------

DAG_CONFIG: Dict[str, Any] = {
    "source_bucket": "my-data-lake",
    "source_prefix": "raw/events/",
    "target_table": "analytics.silver_events",
    "quality_table": "analytics.quality_results",
    "slack_channel": "#data-alerts",
    "owner": "data-engineering",
}

DEFAULT_ARGS: Dict[str, Any] = {
    "owner": DAG_CONFIG["owner"],
    "depends_on_past": False,
    "email_on_failure": True,
    "email_on_retry": False,
    "email": ["data-engineering@example.com"],
    "retries": 2,

`📅 Changelog`

v2.0.0 — Initial release. Last updated 2026-07-01.

Purchases include lifetime updates. Check the product page for the latest version.

`📄 Product Preview`

Try the interactive reader and demo tools below, or get the full product with all content unlocked.

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`❓ Frequently Asked Questions`

What license is this under?

MIT License. You can use it in commercial, client, and internal projects. See our license terms page for details.

How do I download after purchase?

Immediately after purchase, you'll be redirected to a download page. A download link is also sent to your email.

Do I get updates?

Yes. All products include lifetime updates at no extra cost. Re-download anytime for the latest version.

What if it doesn't work for me?

DM @DatanestDigital on X and we'll make it right.

Can I get a refund?

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Is there support?

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