Welcome to apache-airflow-data-engineer Discussions!

[ByteMeDirk]

Apache Airflow for Data Engineers: A Comprehensive Guide to Key Features and Functionalities

Welcome to the Discussions

We are excited to have you here! This repository is dedicated to exploring the key features and functionalities of Apache Airflow, tailored specifically for Data Engineers. We encourage you to:

• Ask questions you may have about Airflow.
• Share your experiences and insights.
• Engage with other members of the community.
• Welcome others and be open-minded.

Apache Airflow: Key Features and Functionalities

Throughout this repository, we will delve into various aspects of Apache Airflow, including:

• Role-Based Access Control (RBAC): Apache Airflow allows for the implementation of RBAC in the web-server UI for secure access control. RBAC assigns specific permissions to each role, which is a combination of resources and actions. For more information.
• Airflow Users: To create a new user, we will use the Airflow CLI commands within our Docker environment. This process involves logging into the Docker console and executing the necessary commands.
• Simple Branching DAG: We will construct a simple branching DAG that allows users to specify parameters, such as 'input_arg'. This DAG will be visible in the User Interface (UI), assuming we're logged in with the 'Admin' user profile. For more information.
• Simple Variable Branching DAG: Airflow offers a valuable feature that allows administrators to manage variables. These variables, defined as key-value pairs, can be utilized by DAGs to shape their processes.
• Airflow Connections: We will utilize the 'Admin' role to establish a SQLite connection and set up a simple SQL Directed Acyclic Graph (DAG) to create a table and insert data.
• Actions, Resources & Permissions: Apache Airflow employs actions, resources, and permissions to establish its Role-Based Access Control (RBAC) for all users.
• Service Level Agreement (SLA) Management: Airflow allows for the configuration of notifications when a Service Level Agreement (SLA) is not met. This setup depends on your email service and client, with a simple setup often involving a Gmail account configured with a Mail App that Airflow can use to send emails.
• Task Level SLAs: Airflow allows for the configuration of notifications when a Service Level Agreement (SLA) is not met at the task level.
• Data-aware DAGs: Data-aware DAGs in Airflow are interconnected through tasks and dependencies. Each task represents a unit of work, while dependencies define the order in which tasks are executed.
• Airflow Plugins: Airflow has a great plugin manager to add external features. Plugins in Airflow are a way to extend the core functionalities of the platform.

We hope that this repository will provide you with valuable insights and practical knowledge about Apache Airflow, its features, and its functionalities. We encourage you to engage with the community, ask questions, and share your experiences.