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Luigi

67
171
+ 1
8
Zenaton

7
11
+ 1
12
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Luigi vs Zenaton: What are the differences?

Developers describe Luigi 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. On the other hand, *Zenaton** is detailed as "Workflow Engine as a Service". It is a toolset for developers and data engineers to run and monitor data processes and asynchronous jobs. It makes it really easy and helps developers to programmatically build, run and scale long-running and distributed workflows.

Luigi and Zenaton can be categorized as "Workflow Manager" tools.

Some of the features offered by Luigi are:

  • dependency resolution
  • workflow management
  • visualization

On the other hand, Zenaton provides the following key features:

  • Monitor every execution
  • Optimize Server Usage
  • Infinitely Scalable

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.

Advice on Luigi and Zenaton
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Apache SparkApache SparkLuigiLuigi
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AirflowAirflow

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.

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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 159.1K views
Recommends
CassandraCassandra

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

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Pros of Luigi
Pros of Zenaton
  • 5
    Hadoop Support
  • 2
    Python
  • 1
    Open soure
  • 3
    Monitoring
  • 3
    Error Handling
  • 2
    Workflows as code
  • 2
    Support
  • 1
    Alerting
  • 1
    Multi-language
  • 0
    Deployment Integration

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What is Luigi?

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.

What is Zenaton?

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

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What companies use Zenaton?
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What are some alternatives to Luigi and Zenaton?
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
GitHub Actions
It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Camunda
It is an open source platform for workflow and decision automation that brings business users and software developers together.
Microsoft Power Automate
It is a service that helps you create automated workflows between your favorite apps and services to synchronize files, get notifications, collect data and more.
See all alternatives