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Airflow vs Databricks Jobs

Airflow vs Databricks Workflows🔗

Both Apache Airflow and Databricks Workflows are used to orchestrate jobs, but they solve different scopes of problems.

High-Level Difference🔗

Aspect Airflow Databricks Workflows
Scope Cross-platform orchestration Databricks-centric orchestration
Primary use Orchestrate many systems Orchestrate Databricks jobs
Language Python DAGs UI + JSON + notebooks
Best at Complex dependencies across tools Spark / Databricks pipelines
Vendor lock-in Low High (Databricks only)

Core Philosophy🔗

Airflow🔗

  • Platform-agnostic orchestrator
  • Controls Spark, dbt, Snowflake, APIs, ML, scripts
  • DAGs live outside compute engines
  • Ideal for enterprise-wide orchestration

Databricks Workflows🔗

  • Native job orchestration inside Databricks

  • Tight integration with:

  • Notebooks

  • Jobs
  • Delta Live Tables
  • Unity Catalog
  • Ideal for lakehouse-only pipelines

Architecture Comparison🔗

Airflow-Centric🔗

Airflow
 ├─ Spark (Databricks, EMR)
 ├─ dbt
 ├─ Snowflake / Redshift
 ├─ APIs
 └─ Notifications

Databricks Workflows-Centric🔗

Databricks Workflows
 ├─ Notebook tasks
 ├─ Spark jobs
 ├─ DLT pipelines
 └─ Databricks SQL

Scheduling & Dependencies🔗

Airflow🔗

  • Explicit task dependencies (task_a >> task_b)

  • Rich scheduling:

  • Cron

  • Backfills
  • Catchup
  • SLA monitoring
  • Cross-DAG dependencies supported

Databricks Workflows🔗

  • Linear or DAG-like job tasks
  • Simple schedules
  • Limited cross-job dependency logic
  • Best for straightforward pipelines

Failure Handling & Observability🔗

Airflow🔗

  • Fine-grained retries per task
  • Task-level logs
  • Clear / rerun specific steps
  • SLA alerts and callbacks

Databricks Workflows🔗

  • Job-level monitoring
  • Notebook logs
  • Less granular retry logic
  • Easier but less flexible

Development Experience🔗

Airflow🔗

  • Code-first (Python)
  • Version controlled
  • More setup overhead
  • Steeper learning curve

Databricks Workflows🔗

  • UI-driven
  • Fast to start
  • Less boilerplate
  • Easier for analytics teams

When to Use Which🔗

Use Airflow when:🔗

  • You orchestrate multiple systems
  • You need complex dependencies
  • You want vendor-neutral orchestration
  • You run enterprise-scale pipelines

Use Databricks Workflows when:🔗

  • Everything runs in Databricks
  • Pipelines are Spark-centric
  • You want minimal orchestration overhead
  • You rely heavily on DLT and notebooks

Common Enterprise Pattern (Best Practice)🔗

Image

Image

Airflow
   ↓ (DatabricksRunNowOperator)
Databricks Workflows
   ↓
Spark / DLT / SQL

Airflow handles:

  • Scheduling
  • Cross-system dependencies
  • Alerts

Databricks handles:

  • Compute
  • Transformations
  • Lakehouse logic

Decision Cheat Sheet🔗

Scenario Recommendation
Company-wide orchestration Airflow
Databricks-only platform Databricks Workflows
Hybrid cloud stack Airflow
Simple Spark pipelines Databricks Workflows
Complex SLAs & dependencies Airflow

One-Line Summary🔗

Use Airflow as the enterprise orchestrator and Databricks Workflows as the execution engine for Spark-based pipelines.

Cost, Scaling, and Failure Trade-offs🔗

Image

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1) Cost Model🔗

Apache Airflow🔗

What you pay for

  • Infrastructure (VMs / Kubernetes nodes)
  • Metadata DB (Postgres / MySQL)
  • Logging (S3, CloudWatch, GCS)
  • Engineering time (maintenance)

Cost characteristics

  • Scheduler and workers are always on
  • Cost is mostly fixed
  • Orchestrating 10 tasks vs 10,000 tasks does not multiply cost linearly

Hidden costs

  • Upgrades
  • Plugin/operator maintenance
  • Scaling scheduler for large DAG counts

Typical monthly pattern

  • Stable, predictable
  • Good for heavy orchestration workloads

Databricks Workflows🔗

What you pay for

  • DBUs for job clusters
  • Cloud compute (per run)
  • Storage and logs

Cost characteristics

  • Pay per job execution

  • Scales linearly with:

  • Number of jobs

  • Runtime
  • Cluster size

Hidden costs

  • Frequent small jobs become expensive
  • Retries mean re-running full clusters

Typical monthly pattern

  • Spiky
  • Sensitive to retry storms and inefficient jobs

2) Scaling Behavior🔗

Airflow Scaling🔗

What scales

  • Number of DAGs
  • Number of tasks
  • Number of parallel executions

Scaling limits

  • Scheduler becomes bottleneck at:

  • Tens of thousands of DAGs

  • Millions of task instances/day

  • Requires tuning:

  • Scheduler heartbeat

  • DAG parsing
  • Executor choice

Good at

  • Many lightweight orchestration steps
  • Cross-team pipelines
  • Enterprise-scale dependency graphs

Databricks Workflows Scaling🔗

What scales

  • Spark compute
  • Data volume
  • Parallel job runs

Scaling limits

  • Each job = separate cluster or task
  • Job orchestration logic is limited
  • UI becomes hard to manage at large job counts

Good at

  • Heavy Spark workloads
  • Data volume scaling
  • CPU/memory intensive pipelines

3) Failure Handling & Blast Radius🔗

Airflow Failure Model🔗

Granularity

  • Task-level failures
  • Retry individual tasks
  • Clear and rerun partial DAGs

Blast radius

  • Small
  • One failed task does not kill entire pipeline

Operational advantage

  • Can fix downstream issues without reprocessing everything
  • Ideal for long multi-step pipelines

Databricks Workflows Failure Model🔗

Granularity

  • Often job or notebook-level
  • Retry usually means rerunning large portions

Blast radius

  • Large
  • Cluster startup + job rerun costs incurred

Operational impact

  • Costly retries
  • Less control for partial recovery

4) Backfills and Reprocessing🔗

Airflow🔗

  • Native support for backfills
  • Re-run historical partitions cleanly
  • Strong date-based semantics (execution_date)

Best for:

  • Regulatory backfills
  • Finance and insurance recomputations
  • Late-arriving data corrections

Databricks Workflows🔗

  • Backfills require:

  • Manual parameterization

  • Custom logic
  • No first-class historical orchestration model

Best for:

  • Forward-only pipelines
  • Idempotent batch jobs

5) Observability & Operations🔗

Airflow🔗

  • DAG-level visibility
  • Task timelines
  • SLA misses
  • Fine-grained logs

Operations-heavy but transparent.

Databricks Workflows🔗

  • Strong Spark-level metrics
  • Cluster metrics
  • Less orchestration visibility

Compute-heavy but simpler operationally.

6) Real Enterprise Pattern (Cost-Optimal)🔗

Image

Image

Airflow
  ├─ Lightweight checks
  ├─ Dependency orchestration
  ├─ SLA & alerts
  ↓
Databricks Workflows
  ├─ Heavy Spark transforms
  ├─ DLT pipelines
  └─ Feature engineering

Why this works:

  • Airflow absorbs orchestration complexity cheaply
  • Databricks only runs when real compute is needed
  • Failures are isolated and cheaper to recover

Decision Matrix🔗

Requirement Better Choice
Many pipelines, light logic Airflow
Heavy Spark processing Databricks Workflows
Complex retries & partial reruns Airflow
Cost control at scale Airflow
Simple Databricks-only stack Databricks Workflows
Regulatory backfills Airflow

Final Takeaway🔗

  • Airflow optimizes orchestration cost, control, and failure isolation
  • Databricks Workflows optimizes compute scalability and execution simplicity
  • Best practice is Airflow orchestrating Databricks, not replacing it