Airflow vs dbt: Do You Need Both?

TL;DR

• Apache Airflow is a general-purpose orchestrator (schedules and runs workflows as DAGs); dbt is a SQL transformation framework (the "T" in ELT, run inside your warehouse). Different layers, different jobs.
• It is usually not either/or. Most data teams run both and let Airflow (or another scheduler) trigger dbt.
• If your only job is transforming data already loaded in a warehouse with SQL, you may need just dbt. If you orchestrate many heterogeneous jobs across tools, you need an orchestrator like Airflow — and probably dbt too.
• Verdict: use both, with a clear division of labor. Neither replaces the other.

Airflow and dbt solve different problems. Apache Airflow is a general-purpose orchestrator that schedules and runs workflows; dbt transforms data already in your warehouse using SQL. They sit at different layers, so it is rarely either/or — most teams run both, with Airflow triggering dbt. Choose dbt for the transformation, Airflow for the orchestration.

What is the difference between Airflow and dbt?

The short version: Airflow decides what runs and when; dbt decides how your data gets transformed.

Apache Airflow is a platform for authoring, scheduling, and monitoring workflows written in Python as DAGs (directed acyclic graphs). It coordinates tasks across any system — extract jobs, ML training, API calls, and yes, dbt runs. It does not care whether a task is SQL, a shell command, or a model-training job.

dbt handles the "T" in ELT. You write SQL models (mostly select statements saved as .sql files; Python models are supported too), and when you run dbt run it executes those transformations inside your warehouse without the data leaving it. dbt also gives you ref()-based lineage, tests, and documentation.

So the "airflow vs dbt" framing is a category error. One orchestrates; the other transforms. The same confusion shows up with pure orchestrators — see our Dagster vs Prefect comparison for how the orchestration layer itself splits.

What is Airflow for?

Airflow is for orchestration: triggering work on a schedule or an event, in the right order, with retries, logging, and alerting when something fails.

You define a DAG, Airflow's scheduler works out the dependency order, and operators run the actual tasks. It is the control plane for a pipeline that spans many tools.

Airflow 3, released in April 2025, added DAG versioning (a run finishes on the version it started with), scheduler-managed backfills from the UI or API, a new Task Execution Interface and Python Task SDK, event-driven scheduling via Data Assets, and a React-based UI. The current line is 3.x. Airflow is an Apache Software Foundation project under the Apache 2.0 license. If you are deciding between Airflow and a workflow engine, our Temporal vs Airflow piece draws that line.

What is dbt for?

dbt is for transformation inside the warehouse. You model raw tables into clean, tested, documented datasets using SQL, and dbt handles dependency ordering between models via ref().

What you get: version-controlled SQL models, automatic lineage graphs, built-in data tests (uniqueness, not-null, and custom assertions), and generated documentation. It is the analytics engineering layer that sits on top of Snowflake, BigQuery, Databricks, Postgres, and similar.

A note on the ecosystem, because it shifted in 2025–2026. dbt Core is the open-source tool under the Apache 2.0 license (dbt-core on GitHub), while dbt Cloud is the commercial managed platform. dbt Labs has introduced a Rust-based engine (branded dbt Fusion) for faster, SQL-aware execution. Separately, dbt Labs and Fivetran completed a merger in June 2026, forming a single company — worth watching if you depend on either tool's roadmap.

Do you need both Airflow and dbt?

Most teams: yes, and they complement each other. dbt transforms; it does not schedule itself in production, trigger upstream extract/load jobs, or orchestrate non-SQL work. Airflow does all of that but does not transform data — you still need something to write the SQL. Run dbt for the models, Airflow for the orchestration around them.

You might need only dbt if your entire workload is "data is already in the warehouse, transform it on a simple schedule," and your warehouse or dbt's own scheduling (dbt platform jobs) covers your timing needs.

You might need only an orchestrator if you have no SQL warehouse transformation step at all — but if you do transform, dbt is usually the better tool than hand-rolled SQL in Airflow tasks.

How do Airflow and dbt work together?

The common pattern: Airflow triggers dbt as one or more tasks in a larger DAG. Extract and load run first, dbt transforms, then downstream tasks (reports, ML, reverse ETL) run.

Three integration approaches:

• BashOperator: run dbt run / dbt test as a shell command. Simplest, but you get low visibility into per-model state, and a failure can force a full rerun.
• Astronomer Cosmos: an open-source package that turns a dbt Core project into an Airflow DAG or task group, creating a separate Airflow task for each dbt model (via DbtTaskGroup or DbtDag). Model dependencies become task dependencies, and dbt tests run right after the model they test. This gives per-model retries and observability.
• dbt platform / API triggering: Airflow kicks off a managed dbt job and polls for completion, keeping transformation execution in dbt's own environment.

Can dbt replace Airflow (or vice versa)?

No, not cleanly.

dbt cannot replace Airflow as a general orchestrator. dbt orders its own models, but it does not schedule unrelated jobs, coordinate across heterogeneous systems, or manage event-driven triggers across your stack. dbt's own scheduling (in the dbt platform) handles dbt jobs — not your whole pipeline.

Airflow cannot replace dbt either. You could write raw SQL in Airflow tasks, but you would lose ref() lineage, tests, docs, and the modular model structure that make dbt worth using. You would be rebuilding a worse dbt.

Airflow vs dbt: which do you actually need?

What this means for you

You are an analytics team transforming SQL in a warehouse. Start with dbt. If a simple schedule and dbt's own jobs cover you, you may not need Airflow yet. Add an orchestrator when you need to coordinate loads, trigger downstream work, or handle failures across tools.

You are a platform team orchestrating many heterogeneous jobs. You need an orchestrator, and Airflow is the mature default. Add dbt for the transformation step rather than writing SQL into Airflow tasks, and wire the two together with Cosmos or a dbt job trigger. If MLOps is the real goal, our roundup of MLOps platforms maps where orchestration fits.

You are a small team starting fresh. Begin with dbt for transformation. Defer Airflow until your scheduling needs outgrow dbt's built-in jobs — do not stand up an orchestrator before you have something to orchestrate. Either way, watch your pipelines with one of these data observability tools.

The verdict

Use both, with a clean division of labor: Airflow orchestrates, dbt transforms. Airflow (or another scheduler) triggers extract/load, then dbt, then downstream jobs; dbt owns the SQL models, tests, and lineage. For running dbt inside Airflow, Cosmos gives you per-model tasks and visibility; a BashOperator is fine for simpler setups.

The genuine single-tool cases are narrow. dbt alone works when everything lives in one warehouse and dbt's own scheduling is enough. A standalone orchestrator alone makes sense only when you have no warehouse SQL transformation step at all. Everyone in between runs both.

Frequently Asked Questions

What is the difference between Airflow and dbt?

Airflow is a general-purpose orchestrator that schedules and runs workflows as Python DAGs across any system. dbt is a transformation framework that runs SQL models inside your data warehouse. Airflow decides what runs and when; dbt decides how your warehouse data gets transformed. They operate at different layers.

Do you need both Airflow and dbt?

Usually yes. dbt transforms data but does not orchestrate upstream loads, trigger non-SQL jobs, or schedule your whole pipeline in production. Airflow orchestrates but does not transform. Most teams run dbt for the SQL modeling and Airflow for the scheduling and coordination around it.

Can dbt replace Airflow?

No. dbt orders its own models and the dbt platform can schedule dbt jobs, but dbt is not a general orchestrator. It cannot coordinate heterogeneous jobs across your stack, manage event-driven triggers, or run non-dbt tasks. For pipeline-wide orchestration you still need a tool like Airflow.

Should I run dbt inside Airflow?

Often, yes. The cleanest pattern is Airflow triggering dbt as part of a larger DAG. Use Astronomer's Cosmos to render each dbt model as its own Airflow task with per-model retries and visibility, or a BashOperator running dbt run for simpler needs. Both keep orchestration and transformation cleanly separated.