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Airflow vs Apache Camel: What are the differences?
Key Differences between Airflow and Apache Camel
Airflow and Apache Camel are two popular frameworks used for building and managing data pipelines and integrating systems. While they serve similar purposes, there are several key differences between the two.
Technology Stack: Airflow is built primarily using Python and leverages its rich ecosystem of libraries and tools. On the other hand, Apache Camel is written in Java and utilizes its extensive support for enterprise integration patterns.
Workflow Orchestration vs. Integration Framework: Airflow is primarily a workflow orchestration tool that focuses on managing and scheduling workflows as a series of tasks. Apache Camel, on the other hand, is an integration framework that enables message routing, transformation, and integration between various systems.
Data Processing Paradigm: Airflow follows a batch processing paradigm, where tasks are executed in predefined intervals or upon event triggers. Apache Camel, on the other hand, supports both batch processing and real-time event-driven processing, making it suitable for a wider range of use cases.
Flexibility vs. Convention: Airflow provides a high degree of flexibility in designing workflows and allows developers to define custom operators and hooks. Apache Camel, on the other hand, follows a convention-over-configuration approach, providing a set of predefined integration patterns and components for developers to use.
Community and Ecosystem: Airflow has a large and active community of users and contributors, resulting in a wide range of connectors, plugins, and integrations available. Apache Camel also has a vibrant community but focuses more on enterprise integration patterns and has a smaller ecosystem compared to Airflow.
Scalability and Deployment: Airflow is designed to scale horizontally and can handle large-scale data pipelines with distributed execution across multiple worker nodes. Apache Camel is also scalable, but its deployment model is typically based on Java application servers, which may have different considerations for scalability and resource management.
In summary, while both Airflow and Apache Camel are powerful frameworks for building data pipelines and integrating systems, Airflow focuses more on workflow orchestration, provides flexibility in workflow design, and has a larger community and ecosystem, while Apache Camel is a feature-rich integration framework with extensive Java integration capabilities and support for real-time event-driven processing.
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
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Apache Camel
- Based on Enterprise Integration Patterns5
- Has over 250 components4
- Free (open source)4
- Highly configurable4
- Open Source3
- Has great community2
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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 Apache Camel
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