Building an Efficient ETL Pipeline with Apache Airflow, Docker, and PostgreSQL

Architecture Breakdown:

The architecture of the ETL pipeline involves the following components:

Dockerized Apache Airflow: Ensures isolation and reproducibility of the workflow environment. Python Scripts for Data Transformation: Utilizes pandas and other libraries to process data efficiently. PostgreSQL as the Data Warehouse: Where transformed data is stored for querying and reporting.

If you're using a Windows system, make sure that virtualization is enabled in the BIOS settings. Without this setting enabled, Docker will not work as expected because Docker relies on virtualization technology to run containers.

Importnet Link : https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html

Here You can find the detailed explanation of the Pipeline: Medium post