Apache Spark VS luigi

Compare Apache Spark vs luigi and see what are their differences.

Apache Spark

Apache Spark - A unified analytics engine for large-scale data processing (by apache)

luigi

Luigi 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. (by spotify)
Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
Apache Spark luigi
101 14
38,249 17,292
1.0% 0.7%
10.0 6.4
5 days ago 4 days ago
Scala Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Apache Spark

Posts with mentions or reviews of Apache Spark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-11.

luigi

Posts with mentions or reviews of luigi. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-21.
  • Ask HN: What is the correct way to deal with pipelines?
    4 projects | news.ycombinator.com | 21 Sep 2023
    I agree there are many options in this space. Two others to consider:

    - https://airflow.apache.org/

    - https://github.com/spotify/luigi

    There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

  • In the context of Python what is a Bob Job?
    2 projects | /r/learnpython | 10 Jul 2022
    Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.
  • Lessons Learned from Running Apache Airflow at Scale
    12 projects | news.ycombinator.com | 23 May 2022
    What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi
  • Apache Airflow. How to make the complex workflow as an easy job
    1 project | dev.to | 20 Feb 2022
    It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks.
  • DevOps Fundamentals for Deep Learning Engineers
    6 projects | /r/deeplearning | 20 Feb 2022
    MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.
  • Data pipelines with Luigi
    4 projects | dev.to | 22 Dec 2021
    At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.
  • Noobie who is trying to use K8s needs confirmation to know if this is the way or he is overestimating Kubernetes.
    3 projects | /r/kubernetes | 20 Oct 2021
  • Open Source ETL Project For Startups
    3 projects | dev.to | 22 Sep 2021
    💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.
  • Resources/tutorials to help me learn about ETL?
    1 project | /r/dataengineering | 29 Jun 2021
  • Using Terraform to make my many side-projects 'pick up and play'
    3 projects | dev.to | 14 Jun 2021
    So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.

What are some alternatives?

When comparing Apache Spark and luigi you can also consider the following projects:

Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services

Scalding - A Scala API for Cascading

Dask - Parallel computing with task scheduling

Pinball

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

streamparse - Run Python in Apache Storm topologies. Pythonic API, CLI tooling, and a topology DSL.