Go with the Flow: Managed Airflow in Azure Data Factory

In the world of data processing and workflow management, Apache Airflow has emerged as a leading open-source solution. With its ability to programmatically author, schedule, and monitor sequences of processes and tasks, it’s no wonder why organizations are turning to this tool to streamline their operations and scale their data pipelines.

But, with so many different tools and technologies available, it can be overwhelming to determine which solution is right for your business. This is where Managed Airflow in Azure Data Factory comes in.

Azure Data Factory offers serverless pipelines for data process orchestration, data movement with 100+ managed connectors, and visual transformations with the mapping data flow. Managed Airflow in Azure Data Factory is a managed orchestration service for Apache Airflow that simplifies the creation and management of Airflow environments. With this service, you can use Airflow and Python to create data workflows without having to manage the underlying infrastructure for scalability, availability, and security.

In this series of blog posts, we will dive deep into the world of Apache Airflow and Managed Airflow in Azure Data Factory. We will explore the basics of Airflow, how it works, and why it’s quickly becoming a go-to solution for organizations of all sizes. Additionally, we will examine the many benefits of using Managed Airflow in Azure Data Factory, including ease of use, scalability, and security.

What is Airflow and why is it useful for data processing pipelines?

What is Airflow and why is it useful for data processing pipelines?

Setting up your first Airflow DAG

Airflow Operators

Using Airflow Variables and Connections

Monitoring and Managing Airflow Workflows

image.png

Whether you’re a seasoned data professional or just getting started in the field, these blog posts will provide you with the information you need to understand the power and potential of Apache Airflow and Managed Airflow in Azure Data Factory.