# Installing and Running Locally — Apache Airflow: Orchestrate Data Pipelines

Source: https://www.geekswithgeeks.com/en/airflow/af-install-run

> Install Airflow in a virtual environment with the official constraints and start it.

## Use a venv and the constraints file

Airflow has many dependencies, so install it in a **virtual environment** with the project's **constraints file** for your Airflow and Python versions; that pins compatible versions and avoids dependency conflicts. For learning, `airflow standalone` initialises the database, creates an admin user and starts all components on one machine. For anything beyond learning, use Docker Compose, the official Helm chart or a managed service (Amazon MWAA, Google Cloud Composer, Astronomer). Set `AIRFLOW_HOME` to choose where config, logs and the DAGs folder live.

## Install with constraints

Replace the version numbers with the release you want. Airflow 3.x requires a supported Python 3 version; check the install docs.

```bash
python3 -m venv af-venv && source af-venv/bin/activate
AIRFLOW_VERSION=3.0.6
PYTHON_VERSION="$(python -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
pip install "apache-airflow==${AIRFLOW_VERSION}" \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"

export AIRFLOW_HOME=~/airflow
airflow standalone          # learning only: DB + admin user + all components
```

## Do not use standalone in production

It uses a local SQLite database and a single process, which is fine for learning but not for concurrent, reliable production use.

**Quiz:** Why install Airflow with a constraints file?

- [ ] It is required to open the UI
- [ ] It makes Airflow free
- [ ] It removes the scheduler
- [x] It pins compatible dependency versions and avoids conflicts

*Answer:* It pins compatible dependency versions and avoids conflicts. Constraints give a tested set of package versions for each release.