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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.
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.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Camunda
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Camunda
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