Building a Scalable Data Pipeline with dbt, Python, Podman, Airflow, and Ansible

Introduction

Managing data pipelines efficiently requires a scalable, automated, and containerized solution. In this article, I will walk you through an architecture that integrates dbt, Python, Podman, Airflow, and Ansible to streamline data transformations, orchestration, and automation.

This setup ensures:

? Automation using Ansible

? Containerized execution via Podman

? Orchestration using Airflow

? Data transformation with dbt & Python

? Scalability through modular design

Let’s break it down. ??

1?? Architecture Overview

At a high level, this architecture consists of:

• Airflow (running inside a Podman container) → Schedules and executes DAGs.
• A separate dbt/Python container → Runs dbt models and Python ETL scripts.
• Podman-Compose → Manages Airflow services.
• Ansible → Automates the entire infrastructure setup.

?? Architecture Diagram

+----------------------------------+
|          ?? Ansible              |
|  (Automates Deployment)          |
+----------------------------------+
              |
              v
+------------------------------------------+
|      ?? Podman (Containerization)        |
|  - Manages Airflow & dbt/Python images  |
|  - Uses Podman-Compose                  |
+------------------------------------------+
         |                     |
         v                     v
+---------------------+   +----------------------+
|      ?? Airflow     |   |  ?? Python/dbt       |
|  (Podman Service)  |   |  (Executes dbt jobs) |
|---------------------|   |----------------------|
| - Schedules DAGs   |   | - Runs dbt models    |
| - Uses DockerOperator | | - Runs Python ETL   |
| - Mounted DAGs     |   |                      |
+---------------------+   +----------------------+
              |
              v
   +-------------------------+
   |       dbt Models        |
   |  - SQL Transformations  |
   |  - Python dbt Models    |
   +-------------------------+
        

2?? Key Components & Technologies

?? Podman (Containerized Execution)

• Two containers:Airflow Container → Runs DAGs and orchestrates tasks.Python/dbt Container → Executes dbt models and Python transformations.
• Podman-Compose manages multi-container setup.
• Volume mounting ensures DAGs are available inside Airflow.

?? Ansible (Automation)

• Automates the entire infrastructure provisioning:Installs Podman & dependenciesDeploys Airflow & dbt/Python containersConfigures DAGs & database connectionsManages environment variables & secrets

?? Airflow (Orchestration)

• Runs inside a Podman container.
• Uses DockerOperator to trigger dbt/Python models in a separate container.
• DAGs are mounted into the Airflow container.

?? dbt & Python Container

• Containerized Python & dbt environment.
• Airflow DAGs trigger dbt inside this container using DockerOperator.

3?? Workflow Execution

Step 1?? - Deployment via Ansible

1. Ansible provisions Podman, installs dependencies, and sets up containers.
2. Airflow DAGs and configurations are copied into the Airflow container.

Step 2?? - Containerized Execution with Podman

1. Podman-Compose launches:
2. DAGs are mounted inside the Airflow container.

Step 3?? - Airflow DAG Execution

1. Airflow schedules the DAG and runs the DockerOperator.
2. DockerOperator triggers the dbt/Python container.

Step 4?? - Data Transformation

1. dbt executes SQL models inside the Python/dbt container.
2. Data is processed and stored in the target database.

Step 5?? - Automated Monitoring & Logging

1. Airflow logs execution details & errors.
2. Podman ensures containers restart on failure.

4?? Deployment Process

?? Step 1: Deploy with Ansible

Run the Ansible playbook to install Podman, deploy Airflow & dbt, and configure everything:

ansible-playbook ansible/main.yml        

?? Step 2: Start Services using Podman-Compose

podman-compose up -d        

?? Step 3: Check Running Containers

podman ps        

?? Step 4: Trigger DAG in Airflow

airflow dags trigger dbt_dag        

5?? Key Advantages

? Completely Automated Using Ansible, the entire deployment process is automated—from installing dependencies to configuring containers.

? Containerized & Scalable By separating Airflow and dbt/Python environments, the system is modular and easy to scale.

? Airflow Orchestration Airflow manages and schedules DAGs while triggering dbt & Python ETL jobs in a separate container.

? Secure & Configurable

• Secrets & environment variables managed efficiently.
• Volume mounting ensures Airflow has access to all necessary DAGs.

? Minimal Overhead

• Podman replaces Docker for efficient container management.
• Containers restart automatically on failure.

6?? Conclusion

This architecture ensures a containerized, automated, and orchestrated data pipeline using:

• ?? Ansible for automation
• ?? Podman for containerization
• ?? Airflow for scheduling
• ?? dbt & Python for transformations

?? Whether you're handling SQL-based dbt transformations or Python ETL scripts, this scalable and modular setup makes the entire pipeline efficient, repeatable, and easy to maintain.

?? What do you think about this approach? Have you used a similar architecture before? Let’s discuss in the comments! ????

7?? Next Steps

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