Databricks Workflows vs Apache Airflow: Which Should You Choose?

If your data platform runs on Databricks, you've inevitably faced the orchestration question: stick with Apache Airflow (the industry standard) or switch to Databricks Workflows (the native, integrated option)?

Both can orchestrate your ETL pipelines. But they have meaningfully different design philosophies, cost structures, and operational burdens. This guide cuts through the noise with a direct technical comparison.

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What Each Tool Is

Apache Airflow is an open-source workflow orchestration platform built around Python DAGs (Directed Acyclic Graphs). Originally from Airbnb, it's now a top-level Apache project with massive community adoption. Most teams run it via managed services: MWAA (AWS), Cloud Composer (GCP), or Astro (Astronomer).

Databricks Workflows is the native job orchestration layer built into the Databricks platform. It runs notebooks, Python scripts, SQL queries, Delta Live Tables pipelines, and dbt projects — all in the same interface you use for development.

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Feature Comparison

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Architecture Deep Dive

Databricks Workflows

A Databricks Workflow is a job with one or more tasks. Tasks can be:

• Notebooks
• Python scripts (wheel or .py)
• SQL queries
• Delta Live Tables pipelines
• dbt projects
• Spark Submit jobs

Tasks are linked with dependency arrows, creating a DAG-like structure — but defined in JSON or via the UI, not Python.

{
  "name": "daily_etl_pipeline",
  "tasks": [
    {
      "task_key": "ingest_raw",
      "notebook_task": {
        "notebook_path": "/Repos/data-team/etl/01_ingest"
      },
      "existing_cluster_id": "0101-123456-abc123"
    },
    {
      "task_key": "transform_silver",
      "depends_on": [{"task_key": "ingest_raw"}],
      "python_wheel_task": {
        "package_name": "my_etl",
        "entry_point": "transform_silver"
      },
      "job_cluster_key": "silver_cluster"
    }
  ],
  "job_clusters": [
    {
      "job_cluster_key": "silver_cluster",
      "new_cluster": {
        "spark_version": "14.3.x-scala2.12",
        "node_type_id": "Standard_D4ds_v5",
        "num_workers": 4
      }
    }
  ],
  "schedule": {
    "quartz_cron_expression": "0 0 2 * * ?",
    "timezone_id": "UTC"
  }
}

Deploy with the Databricks CLI:

databricks jobs create --json @daily_etl_pipeline.json
databricks jobs run-now --job-id 12345

Apache Airflow

Airflow DAGs are pure Python. This gives you full programmatic control:

from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator
from airflow.operators.python import BranchPythonOperator
from datetime import datetime, timedelta

default_args = {
    "owner": "data-team",
    "retries": 3,
    "retry_delay": timedelta(minutes=5),
    "email_on_failure": True,
}

with DAG(
    dag_id="daily_etl_pipeline",
    default_args=default_args,
    schedule_interval="0 2 * * *",
    start_date=datetime(2024, 1, 1),
    catchup=False,
    tags=["databricks", "etl"],
) as dag:

    ingest = DatabricksRunNowOperator(
        task_id="ingest_raw",
        databricks_conn_id="databricks_default",
        job_id=11111,
    )

    def check_data_quality(**context):
        # Dynamic branching based on row counts, schema checks, etc.
        row_count = context["ti"].xcom_pull(task_ids="ingest_raw")
        return "transform_silver" if row_count > 0 else "alert_empty_load"

    branch = BranchPythonOperator(
        task_id="check_quality",
        python_callable=check_data_quality,
    )

    transform = DatabricksRunNowOperator(
        task_id="transform_silver",
        databricks_conn_id="databricks_default",
        job_id=22222,
    )

    ingest >> branch >> transform

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Cost Comparison

Databricks Workflows:

• No orchestration fee — you pay only for the compute (DBUs) used while tasks run
• Job clusters spin up/down per run → cost tied directly to workload
• For Databricks-native pipelines, often the cheapest option

Apache Airflow (MWAA example):

• MWAA small environment: ~$300/month base cost, regardless of usage
• Plus worker compute for task execution
• Plus Databricks DBUs when running Databricks operators

Rule of thumb: If 90%+ of your workloads are Databricks-native, Workflows is almost always cheaper. If you're orchestrating a mix of Databricks, Redshift, Snowflake, APIs, and custom Python, Airflow's total cost may be lower than the operational overhead of multiple native schedulers.

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When to Use Databricks Workflows

✅ Choose Databricks Workflows when:

• All (or most) of your orchestration targets are Databricks jobs
• You want zero infrastructure overhead
• You're already using Delta Live Tables or dbt-on-Databricks
• Your team prefers UI-driven workflow design
• You need tight integration with Unity Catalog lineage
• Fast iteration speed matters (no DAG deployment cycle)

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When to Use Apache Airflow

✅ Choose Apache Airflow when:

• You orchestrate across multiple platforms (Databricks + Snowflake + S3 + APIs)
• You need complex branching, dynamic task generation, or XCom-based state sharing
• Your team is Python-native and prefers code-as-infrastructure
• You need Airflow's extensive provider ecosystem (1000+ integrations)
• Cross-organizational DAG sharing is important
• You require SLA monitoring and advanced alerting built into the scheduler

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The Hybrid Architecture

Many production teams use both:

• Airflow as the top-level orchestrator: handles scheduling, external triggers, cross-system dependencies, alerting
• Databricks Workflows as the execution layer: runs the actual Spark/notebook jobs triggered by Airflow

# Airflow kicks off a Databricks Workflow and waits for completion
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator

run_etl = DatabricksRunNowOperator(
    task_id="run_databricks_etl",
    databricks_conn_id="databricks_default",
    job_id=99999,  # A complex multi-task Databricks Workflow
    wait_for_termination=True,
)

This pattern gives you Airflow's orchestration flexibility without giving up Databricks Workflows' native cluster management and lineage tracking.

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Observability Comparison

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Migration Path

If you're on Airflow and considering Workflows:

# Export existing Airflow DAG schedules and dependencies
# Map each DatabricksSubmitRunOperator → Databricks Workflow task
# Recreate branching logic as Databricks conditional tasks
# Test in dev workspace before cutover

# Useful CLI for bulk job creation from YAML definitions
databricks bundle deploy --target prod

The Databricks Asset Bundles (DAB) framework is the modern way to define Workflows as code, giving you Git-based versioning similar to Airflow's DAG-as-code approach.

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Conclusion

Neither tool wins universally. Databricks Workflows is the right choice for teams going all-in on the Databricks lakehouse — it's simpler, cheaper, and deeply integrated. Airflow is the right choice when you're orchestrating a heterogeneous data ecosystem and need maximum flexibility.

For most teams building a modern data lakehouse on Databricks, start with Workflows. Add Airflow only when you hit its limitations.

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