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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Digdag vs Luigi

Digdag vs Luigi

OverviewDecisionsComparisonAlternatives

Overview

Luigi
Luigi
Stacks78
Followers211
Votes9
GitHub Stars18.5K
Forks2.4K
Digdag
Digdag
Stacks17
Followers22
Votes0
GitHub Stars1.3K
Forks230

Digdag vs Luigi: What are the differences?

  1. Workflow Definition Language: Digdag uses a YAML-based workflow definition language, simplifying the process of writing and understanding workflows. On the other hand, Luigi uses Python code for defining workflows, offering more flexibility and power but can be more complex for beginners.
  2. Ease of Use: Digdag is designed for ease of use and quick setup, making it ideal for small to medium-sized projects. Luigi, on the other hand, provides more advanced features but has a steeper learning curve, making it better suited for large and complex projects.
  3. Execution Engine: Digdag comes with its own built-in execution engine, making it self-contained and easy to deploy. In contrast, Luigi relies on the Luigi scheduler and other external dependencies, which may require additional setup and configuration.
  4. Community and Ecosystem: Luigi has a larger and more active community behind it, with a bigger ecosystem of plugins and extensions available for users. Digdag, while gaining popularity, may have a smaller support network and fewer resources available.
  5. Error Handling: Digdag offers more robust error handling mechanisms, making it easier to troubleshoot and recover from failures in workflows. Luigi, while capable of handling errors, may require more manual intervention and custom coding for error recovery.
  6. Integration Capabilities: Digdag provides native support for various data sources and integrations, simplifying the process of connecting workflows with external systems. In comparison, Luigi may require more custom scripting or third-party tools for integration with different platforms.

In Summary, Digdag and Luigi differ in their workflow definition language, ease of use, execution engine, community support, error handling, and integration capabilities.

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Advice on Luigi, Digdag

Anonymous
Anonymous

Jan 19, 2020

Needs advice

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.

294k views294k
Comments

Detailed Comparison

Luigi
Luigi
Digdag
Digdag

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.

It is a simple tool that helps you to build, run, schedule, and monitor complex pipelines of tasks. It handles dependency resolution so that tasks run in series or in parallel.

dependency resolution; workflow management; visualization
Multi-Cloud;Multi-lingual;Error handling; Modular; Extensible;Admin UI
Statistics
GitHub Stars
18.5K
GitHub Stars
1.3K
GitHub Forks
2.4K
GitHub Forks
230
Stacks
78
Stacks
17
Followers
211
Followers
22
Votes
9
Votes
0
Pros & Cons
Pros
  • 5
    Hadoop Support
  • 3
    Python
  • 1
    Open soure
No community feedback yet
Integrations
Hadoop
Hadoop
Python
Python
Dropbox
Dropbox
Google Drive
Google Drive
Hadoop
Hadoop
WordPress
WordPress
Jira
Jira
Python
Python

What are some alternatives to Luigi, Digdag?

Airflow

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

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

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

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.

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

Shipyard

na

iLeap

iLeap

ILeap is a low-code app development platform to build custom apps and automate workflows visually, helping you speed up digital transformation.

AI Autopilot

AI Autopilot

Agentic AI Platform for Intelligent IT Automation built by MSPs for MSPs. Revolutionize your operations with advanced AI agents.

PromptX

PromptX

PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.

Camunda

Camunda

Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation.

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