What is Mage?
Mage (the mage-ai project) is an open-source, Apache 2.0 licensed tool for building, running, and scheduling data pipelines. Pipelines are built from blocks of Python, SQL, or R code in a browser-based, notebook-style editor, and Mage handles the dependencies between blocks, scheduling, and run history. The company behind it also sells Mage Pro, a commercial managed platform with features such as role-based access control and multi-environment orchestration.
Mage positions itself as an easier alternative to Apache Airflow for teams that want to see data at each step while developing a pipeline.
How Mage Works
- Blocks. Each step is a block with a role: data loaders read from sources, transformers change data, and data exporters write results. Blocks can be previewed interactively, so you see sample output as you write code.
- Pipelines as a graph. Blocks are connected into a directed acyclic graph (DAG); Mage runs them in dependency order and passes outputs between them.
- Triggers. Pipelines run on schedules (including cron expressions), on events, or through API calls.
- Pipeline types. Besides standard batch pipelines, Mage supports streaming pipelines and data integration pipelines that sync sources to destinations.
- dbt support. dbt models can be added and run as blocks inside a pipeline.
Like other orchestrators, Mage often triggers heavy work in other systems. In a lakehouse, a block might call Spark or Dremio to run a MERGE INTO on an Apache Iceberg table, or use PyIceberg or DuckDB for lighter jobs, while Mage tracks the run and retries failures.
Mage vs Airflow vs Dagster vs Prefect
| Mage | Apache Airflow | Dagster | Prefect | |
|---|---|---|---|---|
| Model | Blocks in a notebook-style editor | Task DAGs in Python | Software-defined assets | Python flows and tasks |
| Governance | Company-led open source | Apache Software Foundation | Company-led open source | Company-led open source |
| Strength | Fast interactive development, built-in data previews | Largest ecosystem of operators and providers, widely deployed | Asset lineage, data-aware scheduling, testing | Dynamic workflows with plain Python |
| Commercial offering | Mage Pro | Several managed services (Astronomer, AWS MWAA, Google Cloud Composer) | Dagster+ | Prefect Cloud |
| Watch out for | Smaller community and ecosystem; recent product focus is on Mage Pro | Operational overhead at scale | Learning the asset model | Self-hosting the server for production |
A practical way to choose: if your team already runs Airflow well, Mage rarely justifies a migration. If you are starting fresh with a small team that writes a lot of Python and SQL interactively, Mage or Dagster can be quicker to adopt. For a broader comparison, see Orchestration in 2026: Airflow 3 vs Dagster vs Prefect vs Event-Driven and Introduction to Data Engineering Concepts: Scheduling and Workflow Orchestration.
Frequently Asked Questions
Does an orchestrator like Mage process data directly?
It can, because blocks run Python code in Mage’s workers. For large data, the usual pattern is to have blocks trigger external engines such as Spark, Dremio, or a cloud warehouse and let those do the heavy lifting.
Why use an orchestrator instead of cron jobs?
Cron only knows about time. Orchestrators add dependency ordering, retries, backfills, run history, and alerting, which pipelines need once more than a few jobs depend on each other. See orchestration for the general concept.
Is Mage still maintained?
The open-source mage-ai package continues to publish releases (version 0.9.79 shipped in January 2026), while much of the company’s recent release activity centers on Mage Pro. Check the GitHub repository’s recent activity before committing to it for a new platform.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.