Table of contents
Scalability
Luigi is designed to handle large-scale data pipelines and can manage complex workflows efficiently by breaking them down into smaller tasks.
Task Dependencies
Luigi automatically handles task dependencies and execution order, ensuring that tasks run in the correct sequence based on their dependencies.
Integration
It easily integrates with various data sources and processing frameworks, allowing seamless data flow across different platforms.
Visualization
Provides tools to visualize the workflow and the status of various tasks, helping users to monitor and debug data pipelines effectively.
Extensible
Luigi is highly extensible, allowing developers to write custom tasks to fit specific requirements, enhancing its flexibility.
We have collected here some useful links to help you find out if Luigi is good.
Check the traffic stats of Luigi on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Luigi on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Luigi's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Luigi on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Luigi on Reddit. This can help you find out how popualr the product is and what people think about it.
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... - Source: Hacker News / almost 3 years ago
Maybe if your use case is โsmallishโ and doesnโt require the whole studio suite you could check out apscheduler for doing python โtasksโ on a schedule and luigi to build pipelines. Source: about 4 years ago
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. - Source: Hacker News / about 4 years ago
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. - Source: dev.to / over 4 years ago
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,... Source: over 4 years ago
At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi. - Source: dev.to / over 4 years ago
๐กใAbout Luigiใ 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. - Source: dev.to / almost 5 years ago
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... - Source: dev.to / about 5 years ago
I have an invoke script and now it needs some make-like functionality. I could use make to call some invoke tasks, but I'm looking for a more integrated approach. Does anybody have experience using invoke with python-based build automation tools like doit or luigi? Source: over 5 years ago
The public opinion surrounding Luigi, an open-source workflow orchestration tool developed by Spotify, presents a range of perspectives reflecting both its strengths and limitations based on user experiences and mentions in the technical community. Luigi is frequently discussed in the context of workflow automation, particularly when compared to other prominent tools in the field, such as Apache Airflow, Dagster, and Prefect.
Simplicity and Rapid Setup: Luigi is praised for its ability to quickly automate simple ETL processes. It is recommended for tasks like handling logs where complex configurations are unnecessary. Its straightforward Python-based API enables users to define pipelines by extending Task classes using three primary methods: requires, output, and run.
Batch Processing Capabilities: Many users appreciate Luigi's robust handling of long-running batch processes. It facilitates the automatic execution of complex data processing tasks across multiple operations, particularly beneficial for managing dependencies.
Open Source and Community Support: As an open-source tool, Luigi benefits from a supportive community and extensive documentation. It remains a preferred choice for developers looking to integrate workflow orchestration in their Python-based projects without incurring licensing costs.
Scalability Challenges: A recurrent critique of Luigi is its scalability limitations. Unlike some of its competitors, Luigi prevents tasks from running independently, which can be a significant constraint when dealing with large-scale distributed systems. This architectural decision impacts its efficiency for larger, more complex workflows.
Feature Set Limitations: Users have highlighted that Luigi's feature set is not as comprehensive as competitors like Apache Airflow. For newcomers, Luigi's less extensive API might necessitate a steeper learning curve when implementing more sophisticated workflows.
Strict Pipeline Structure: Luigi adopts a pipeline-like structure, which, while beneficial for certain streamlined processes, may become restrictive for more intricate and dynamic data pipeline requirements. This rigidity can limit adaptiveness in rapidly changing environments.
Luigi has been effectively utilized in various industries to manage data transformations and orchestrate automated workflows. At Spotify, its original developer, Luigi is employed for data insights, recommendation engines, A/B testing analysis, and dashboarding tasks. The tool also finds applications in machine learning and natural language processing environments, as evidenced by its use at Wonderflow.
Despite the emergence of newer competitors like Dagster and Prefect, Luigi persists as a favored alternative for workflow orchestration, especially within organizations that value its integration capabilities with Python and its suitability for well-defined task dependencies.
In conclusion, while Luigi might not offer the scalability and feature richness that some competitors provide, it remains a compelling option for straightforward ETL processes and batch processing needs. As with any tool, careful consideration of specific workflow requirements and constraints is essential to determine whether Luigi aligns with organizational goals and infrastructure.
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