DATA PIPELINES WITH APACHE AIRFLOW INSTALL
It takes around a couple of minutes to install the package. You can install any provider package by editing the airflow environment from the Azure Data Factory UI.
Supported Apache Airflow versionsĪpache Airflow integrates with Microsoft Azure services through microsoft.azure provider. The Airflow environment region is defaulted to the Data Factory region and is not configurable, so ensure you use a Data Factory in the above supported region to be able to access the Managed Airflow preview. It also provides metrics to track critical conditions and help you notify if the need be.īy GA, all ADF regions will be supported. Azure Monitoring and alerting – All the logs generated by Managed Airflow is exported to Azure Monitor.It also supports double encryption with a Customer-Managed Key (CMK). Metadata encryption – Managed Airflow automatically encrypts metadata using Azure-managed keys to ensure your environment is secure by default.Managed Virtual Network integration (coming soon) – You can access your data source via private endpoints or on-premises using ADF Managed Virtual Network that provides extra network isolation.Azure Active Directory integration – You can enable Azure AD RBAC against your Airflow environment for a single sign on experience that is secured by Azure Active Directory.Cloud scale – Managed Airflow automatically scales Apache Airflow nodes when required based on range specification (min, max).Fast and simple deployment – You can quickly and easily set up Apache Airflow by selecting an Apache Airflow version when you create a Managed Airflow.Managed Airflow in Azure Data Factory offers a range of powerful features, including: With Managed Airflow, Azure Data Factory now offers multi-orchestration capabilities spanning across visual, code-centric, OSS orchestration requirements. On the contrary, if you wouldn't like to write/ manage python-based DAGs for data process orchestration, you may prefer to use pipelines. If you have the Airflow background, or are currently using Apache Airflow, you may prefer to use the Managed Airflow instead of the pipelines. While Managed Airflow, offers Airflow based python DAGs (python code-centric authoring) for defining the data orchestration process. It's widely used in data engineering and data science to orchestrate data pipelines, and is known for its flexibility, extensibility, and ease of use.Īzure Data Factory offers Pipelines to visually orchestrate data processes (UI-based authoring). Airflow enables you to execute these DAGs on a schedule or in response to an event, monitor the progress of workflows, and provide visibility into the state of each task. It allows you to define a set of tasks, called operators, that can be combined into directed acyclic graphs (DAGs) to represent data pipelines. Documentation and more tutorials for Airflow can be found on the Apache Airflow Documentation or Community pages.Īzure Data Factory offers serverless pipelines for data process orchestration, data movement with 100+ managed connectors, and visual transformations with the mapping data flow.Īzure Data Factory's Managed Airflow service is a simple and efficient way to create and manage Apache Airflow environments, enabling you to run data pipelines at scale with ease.Īpache Airflow is an open-source platform used to programmatically create, schedule, and monitor complex data workflows.
Managed Airflow for Azure Data Factory relies on the open source Apache Airflow application.
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