How to Build a Data Pipeline with Apache Airflow

Yesi Days
4 min readFeb 28, 2023

Apache Airflow is an open-source workflow management tool designed for ETL/ELT (extract, transform, load/extract, load, transform) workflows. It enables users to define, schedule, and monitor complex workflows, with the ability to execute tasks in parallel and handle dependencies between tasks.

I have used it for different workflows, from the simplest to complex and scalable, and in both cases it has many advantages and also details to take care of.

Apache Airflow has a modular architecture, making it easy to add new operators and integrations with external systems. Additionally, it provides a web-based UI for visualizing and monitoring workflow execution. It also includes a rich set of APIs and command-line tools for managing workflows programmatically.

One key benefit of Apache Airflow is its ability to handle complex workflows with ease. This includes workflows involving multiple data sources, transformations, and destinations. Additionally, it provides a robust scheduling engine that can handle failures and retries, ensuring that workflows are completed reliably and on time.

Apache Airflow 2.0 is the latest version and I invite you to review its official documentation. This new version brings many improvements and new features that make the tool even more powerful and user-friendly for managing ETL/ELT workflows.

One of the major changes in Apache Airflow 2.0 is the redesigned UI, which is more intuitive and user-friendly. The new UI includes a drag-and-drop interface for creating workflows and a graph view that makes it easier to visualize and manage complex workflows.

Apache Airflow 2.0 introduces several major improvements, including support for cloud-native features like Kubernetes. This allows for easy deployment and scaling of Airflow on cloud platforms such as Google Cloud Platform and Amazon Web Services. For me, this is the best functionality because it allows you to decide which cloud you should work in and takes work away from having to solve it on your part as was done in the previous version.

In addition, Apache Airflow 2.0 includes new integrations with popular data warehousing and processing tools like Snowflake and Databricks. This makes it easier to manage complex data…



Yesi Days

GDE Machine Learning | Data Scientist | PhD in Artificial Intelligence | Content creator | Ex-backend