Overview

Camunda

Stacks191

Followers216

Votes0

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Camunda: What are the differences?

Airflow and Camunda are both popular workflow management systems. Let's explore the key differences between them.

Execution Model: One major difference between Airflow and Camunda lies in their execution models. Airflow follows a task-based execution model where tasks are defined and executed in a sequential manner. On the other hand, Camunda employs a process-based execution model where business processes are defined using BPMN (Business Process Model and Notation) and executed in a stateful manner. While Airflow is suitable for linear workflow execution, Camunda is designed for complex and dynamic process orchestration.
Workflow Visualization: Airflow provides a built-in UI for visualizing and monitoring workflows. It offers a graphical representation of the workflow structure and the status of each task. Camunda also offers a similar feature but with more advanced capabilities. Camunda provides a comprehensive process modeler that allows users to design, simulate, and visualize workflows in BPMN. Additionally, Camunda provides real-time monitoring and reporting features to track the execution progress of workflows.
Task Scheduling: Airflow has a built-in task scheduling mechanism that allows users to define dependencies between tasks and execute them based on certain conditions. Users can schedule tasks to run at specific times or intervals using cron-like expressions. Camunda, on the other hand, provides more advanced task scheduling capabilities. It supports time-based, event-based, and condition-based task triggers, providing more flexibility in defining task execution patterns.
User Interaction and Forms: Camunda excels in providing user interaction and form capabilities within workflows. It allows users to define user tasks with forms and gather input from users during the workflow execution. This makes Camunda suitable for scenarios where human interaction is required, such as approval processes or user task assignments. Airflow, on the other hand, does not have built-in support for user interaction and forms, focusing primarily on automated task execution.
Integration and Extensibility: Airflow provides a rich set of integrations and extensions, making it easy to connect with different systems and tools. It has a wide range of operators and hooks that enable integration with databases, cloud services, and various third-party tools. Camunda, being a comprehensive BPM platform, also offers extensive integration capabilities. It provides connectors and APIs to interact with other systems, databases, and services, making it suitable for enterprise workflow automation scenarios.
Domain-Specific Features: Each of these workflow management systems has certain domain-specific features that make them stand out. Airflow, being focused on data pipeline orchestration, provides built-in support for data processing tasks and integration with popular data processing frameworks like Apache Spark and Apache Hadoop. Camunda, with its BPMN-based approach, offers advanced capabilities for complex process modeling, decision management, and case management.

In summary, Airflow is a task-based data pipeline orchestration tool with basic workflow visualization and scheduling capabilities. On the other hand, Camunda is a comprehensive BPMN-based workflow and decision automation platform with advanced workflow modeling, user interaction, and extensibility features.

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A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb

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Advice on Camunda, Airflow

Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Detailed Comparison

Camunda	Airflow
Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation.	Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Agentic orchestration; Process orchestration; Workflow automation; Decision automation	Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
Stacks 191	Stacks 1.7K
Followers 216	Followers 2.8K
Votes 0	Votes 128
Pros & Cons
No community feedback yet	Pros 53 Features 14 Task Dependency Management 12 Cluster of workers 12 Beautiful UI 10 Extensibility Cons 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 2 Running it on kubernetes cluster relatively complex 1 Logical separation of DAGs is not straight forward
Integrations
Twilio SendGrid Asana HubSpot Slack GitLab ActiveMQ RabbitMQ Kafka Redis Google Cloud VPC	No integrations available

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