Skip to main content

Airflow

Apache Airflow is an open-source workflow automation and orchestration platform that helps organizations schedule, monitor, and manage complex data workflows. It is a popular choice for organizations of all sizes to handle data pipelines, ETL processes, and task automation.

Key Features of Apache Airflow​

DAGs (Directed Acyclic Graphs)​

Airflow allows you to define workflows using Directed Acyclic Graphs, where each node represents a task, and the edges define the sequence and dependencies between tasks. In Prophecy, you define these DAGs as Airflow Jobs. Each Airflow Job represent a DAG in Airflow.

Dynamic Workflow Scheduling​

Airflow provides a flexible scheduling system that allows you to set up complex dependencies, retries, and time-based triggers for your tasks. All these can be set in the settings page for a Job.

Extensibility​

Airflow is highly extensible, allowing you to define custom operators and sensors to suit your specific needs. It supports a wide range of plugins and integrations. Prophecy maintains this extensibility via Package Hub. Users can create Gems to represent any custom Operator or Sensor.

Monitoring and Logging​

It offers built-in tools for monitoring task progress, logging, and alerting. You can easily track the status of your workflows through a web-based UI. Users would be able to monitor and debug these Jobs directly via Prophecy UI as well.

Parallel Execution and Scalability​

Airflow can execute multiple tasks in parallel. It can also scale horizontally to accommodate high workloads by adding more worker nodes.

Low Code Airflow in Prophecy​

Prophecy simplifies orchestration with a low-code layer on top of Airflow, offering ease and efficiency. What the user builds visually in Prophecy, turns into high-quality and open-source Airflow code on Git. You have the flexibility to integrate and utilize Prophecy with your managed Airflow. If you are new to Airflow, We also offer a Prophecy-managed Airflow, which expedites your setup without having to manage your own Airflow instance.

Key features and components of Low-Code Airflow in Prophecy​

  • Setup: Seamlessly connect to your Airflow Instance, or use Prophecy hosted Airflow for getting started.

  • Development: A visual drag-and-drop interface that allows users to design Jobs by connecting Gems and defining dependencies effortlessly.

  • Scheduling and Triggers: Easily set up schedules for your Jobs and define triggers to automate the execution.

  • Monitoring and Reporting: Monitor the progress of your Jobs, receive notifications on status, and access logs and reporting features for real-time insights into Jobs execution.

  • Extensibility: For advanced users, Low-Code Airflow in Prophecy offers scripting capabilities and Gem builder to accommodate custom requirements.

This combination of low-code development and the power of Airflow enables organizations to streamline data pipelines, reduce development time, and improve operational efficiency.

Benefits of Using Low-Code Airflow in Prophecy​

Low-Code Airflow in Prophecy brings numerous advantages to data engineers, analysts, and organizations:

  • Simplified Workflow Development: With a user-friendly visual interface, even users with limited programming experience can create and manage complex data workflows, reducing the learning curve.

  • Accelerated Development: Rapidly build, test, and deploy Jobs, reducing the time and effort needed to set up and execute data processes.

  • Reduced Maintenance: Low-code Jobs are easier to maintain and update, as they require less coding, resulting in fewer errors and quicker adjustments.

  • Centralized Management: Streamline the management of workflows by having them all in one platform, providing a unified view of your data Pipeline.

  • Automation and Scheduling: Automate data processes and set schedules for tasks, ensuring that critical data operations occur at the right time.

  • Error Handling and Monitoring: Easily track task execution, set up error handling, and receive alerts when issues arise, improving data Pipeline reliability.

  • Cost Savings: Low-Code Airflow can lead to cost savings through increased operational efficiency and decreased development time.

  • Scalability: As your data processing needs grow, Low-Code Airflow can scale with your organization, handling more extensive and complex Jobs.

Let's delve deeper into the specifics of utilizing low-code Airflow.