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Luigi vs Microsoft Power Automate: What are the differences?
Scalability: Luigi is known for its scalability, allowing parallel task execution, while Microsoft Power Automate has limitations on parallelism due to its cloud-based execution model, which can impact performance in large-scale workflows.
Platform Integration: Luigi integrates well with various data platforms like Hadoop, Spark, and Redshift, providing extensive support for ETL processes, whereas Microsoft Power Automate offers seamless integration with Microsoft's ecosystem of productivity tools like Office 365, SharePoint, and Dynamics 365, making it ideal for users already on that platform.
Code-centric vs. No-code Approach: Luigi follows a code-centric approach where workflows are defined in Python scripts, giving developers more control and flexibility in workflow design, unlike Microsoft Power Automate, which follows a no-code approach using a visual workflow designer, making it more user-friendly for non-technical users.
Customization Options: Luigi allows for extensive customization through Python code, enabling users to build complex workflows with advanced logic, whereas Microsoft Power Automate provides a limited set of pre-built connectors and actions, restricting the level of customization available to users.
Pricing Structure: Luigi is an open-source project, making it free to use with no cost associated with the software itself, while Microsoft Power Automate offers a subscription-based pricing model, with different tiers based on the features and usage limits required.
Community Support: Luigi has a strong community of developers contributing to its growth and offering support through forums and documentation, whereas Microsoft Power Automate benefits from the extensive resources and support provided by Microsoft's official channels, ensuring users have access to comprehensive help and guidance.
In Summary, Luigi and Microsoft Power Automate differ in terms of scalability, platform integration, approach to workflow design, customization options, pricing, and community support.
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 Luigi
- Hadoop Support5
- Python3
- Open soure1
