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Camunda vs Luigi: What are the differences?
Developers describe Camunda as "A Workflow and Decision Automation Platform". It is an open source platform for workflow and decision automation that brings business users and software developers together. On the other hand, Luigi is detailed as "*ETL and data flow management library *". It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Camunda and Luigi can be primarily classified as "Workflow Manager" tools.
Luigi is an open source tool with 12K GitHub stars and 1.98K GitHub forks. Here's a link to Luigi's open source repository on GitHub.
According to the StackShare community, Camunda has a broader approval, being mentioned in 4 company stacks & 7 developers stacks; compared to Luigi, which is listed in 6 company stacks and 3 developer stacks.
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 Camunda
Pros of Luigi
- Hadoop Support5
- Python3
- Open soure1