StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Apache Beam vs Luigi

Apache Beam vs Luigi

OverviewDecisionsComparisonAlternatives

Overview

Luigi
Luigi
Stacks78
Followers211
Votes9
GitHub Stars18.5K
Forks2.4K
Apache Beam
Apache Beam
Stacks183
Followers361
Votes14

Apache Beam vs Luigi: What are the differences?

## Key Differences between Apache Beam and Luigi

Apache Beam and Luigi are both popular tools used for building data pipelines, but they have some key differences that distinguish them from each other. 

1. **Execution Model**: Apache Beam utilizes a unified model for defining and executing batch and streaming data processing jobs, making it easier to write code that can run on different distributed processing backends like Apache Flink, Apache Spark, and Google Cloud Dataflow. On the other hand, Luigi is primarily focused on managing batch jobs and orchestrating dependencies between tasks in a linear fashion, which may limit its suitability for streaming data processing scenarios.

2. **Language Support**: Apache Beam supports multiple programming languages such as Java, Python, Go, and more, providing developers with flexibility in choosing the language they are most comfortable with for building data pipelines. In contrast, Luigi is mainly Python-based, which can be a limitation for organizations that require support for other languages in their data pipeline development.

3. **Community and Ecosystem**: Apache Beam has a larger community and ecosystem compared to Luigi, with extensive documentation, support, and third-party tools available for building and managing data pipelines. This broader ecosystem can be beneficial for developers looking to leverage existing solutions and best practices in their pipeline development efforts.

4. **Fault Tolerance**: Apache Beam offers robust fault tolerance capabilities through its processing backends, ensuring that data processing jobs can recover from failures and resume processing without data loss. While Luigi also provides some fault tolerance features, its focus on batch processing may not offer the same level of resiliency as Apache Beam in handling complex distributed data processing workflows.

5. **Scalability**: Apache Beam is designed with scalability in mind, allowing developers to easily scale their data processing jobs horizontally by adding more processing resources as needed. Luigi, on the other hand, may face limitations in scaling to handle large volumes of data or complex processing requirements, making it more suitable for smaller-scale data pipelines.

6. **Integration with External Systems**: Apache Beam provides seamless integration with various external systems and data sources, enabling developers to ingest and process data from a wide range of sources with ease. While Luigi also supports integration with external systems, its focus on simplicity and task dependency management may result in additional complexity when dealing with diverse data sources and systems.

In Summary, Apache Beam and Luigi differ in their execution models, language support, community size, fault tolerance, scalability, and integration with external systems, making each tool suitable for specific data pipeline development needs.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Luigi, Apache Beam

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
Apache Beam
Apache Beam

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

dependency resolution; workflow management; visualization
-
Statistics
GitHub Stars
18.5K
GitHub Stars
-
GitHub Forks
2.4K
GitHub Forks
-
Stacks
78
Stacks
183
Followers
211
Followers
361
Votes
9
Votes
14
Pros & Cons
Pros
  • 5
    Hadoop Support
  • 3
    Python
  • 1
    Open soure
Pros
  • 5
    Open-source
  • 5
    Cross-platform
  • 2
    Unified batch and stream processing
  • 2
    Portable
Integrations
Hadoop
Hadoop
Python
Python
No integrations available

What are some alternatives to Luigi, Apache Beam?

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.

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.

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.

AI Autopilot

AI Autopilot

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

Camunda

Camunda

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

Workflowy

Workflowy

It is an organizational tool that makes life easier. It's a surprisingly powerful way to take notes, make lists, collaborate, brainstorm, plan and generally organize your brain.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase