Need advice about which tool to choose?Ask the StackShare community!
Airflow vs Xplenty: What are the differences?
<Write Introduction here>
- Scalability: Airflow is highly scalable as it allows users to easily scale their workflows by adding more worker nodes, whereas Xplenty has limitations on workflow scalability due to its cloud-based execution model.
- Customization: Airflow provides a higher level of customization with the ability to write custom plugins and operators, while Xplenty has a more restricted set of connectors and transformations for data processing.
- Community Support: Airflow has a large and active community offering extensive documentation, tutorials, and support forums, whereas Xplenty has a smaller community and limited resources for troubleshooting and assistance.
- Ecosystem Integration: Airflow seamlessly integrates with various external services and tools such as Kubernetes, AWS, and Slack, enabling users to build versatile data pipelines, while Xplenty has limited integrations and dependencies on its own platform for data processing.
- Dynamic Task Dependency: Airflow allows for dynamic task dependency configuration based on task outcomes and runtime conditions, providing more flexibility in workflow design, whereas Xplenty has a more static task dependency model with limited options for dynamic scheduling.
- Cost: Airflow is an open-source project with no licensing fees, making it cost-effective for organizations, whereas Xplenty is a paid service with subscription-based pricing, adding to the operational costs for data processing needs.
In Summary, Airflow and Xplenty differ in terms of scalability, customization, community support, ecosystem integration, dynamic task dependency, and cost.
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 Xplenty
- Simple, easy to integrate/process data without coding2
Sign up to add or upvote prosMake informed product decisions
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