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  1. Stackups
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  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs Apache Oozie

Airflow vs Apache Oozie

OverviewDecisionsComparisonAlternatives

Overview

Apache Oozie
Apache Oozie
Stacks40
Followers76
Votes0
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Apache Oozie: What are the differences?

Introduction

Airflow and Apache Oozie are both widely used workflow management systems, with the aim of scheduling and orchestrating complex processes. While they share some similarities, there are key differences that set them apart from each other. In this section, we will explore and highlight the six main differences between Airflow and Apache Oozie.

  1. Architecture: Airflow follows a distributed architecture model and is built on a scalable message queuing system, providing high availability and fault tolerance. On the other hand, Oozie uses a centralized architecture with a single server managing the workflow execution, which may limit scalability for larger deployments.

  2. Workflow Design: Airflow uses Python-based scripting to define workflows, which offers greater flexibility and customizability. Oozie, on the other hand, relies on XML-based configuration files, which although provides a certain level of portability, can be more verbose and less intuitive for developers.

  3. User Interface: Airflow has a web-based user interface that allows users to easily monitor and manage workflows, providing real-time insights into job statuses, monitoring graphs, and logs. Oozie, on the other hand, lacks a user interface and relies primarily on command-line tools or external plugins for monitoring and managing workflows, which can make it less user-friendly for non-technical users.

  4. Ease of Deployment: Airflow can be easily deployed using containerization platforms like Docker, with pre-built images available, simplifying the setup and deployment process. Oozie, on the other hand, requires setting up and configuring various components of the Hadoop ecosystem, making it a more complex and time-consuming deployment process.

  5. Integration with Ecosystem: Airflow has a wide range of integrations with popular data processing frameworks and services, allowing seamless integration into existing data pipelines. Oozie, on the other hand, is tightly integrated with the Hadoop ecosystem, making it a better choice for organizations heavily relying on Hadoop technologies.

  6. Community Support and Development: Airflow has gained significant popularity in recent years, with a large and active open-source community contributing to its development and maintenance. This translates into frequent updates, bug fixes, and new features being regularly released. Oozie, on the other hand, has seen a decline in community support, with fewer updates and new features being introduced, making it less likely to keep up with evolving technologies and requirements.

Summary

In summary, Airflow and Apache Oozie differ in their architecture, workflow design, user interface, ease of deployment, integration with the ecosystem, and community support. These differences make each system better suited for different scenarios and requirements.

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Advice on Apache Oozie, Airflow

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.

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Comments

Detailed Comparison

Apache Oozie
Apache Oozie
Airflow
Airflow

It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path.

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.

-
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
Stacks
40
Stacks
1.7K
Followers
76
Followers
2.8K
Votes
0
Votes
128
Pros & Cons
No community feedback yet
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Apache Oozie, Airflow?

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.

Luigi

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.

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

Flumio

Flumio

Flumio is a modern automation platform that lets you build powerful workflows with a simple drag-and-drop interface. It combines the power of custom development with the speed of a no-code/low-code tool. Developers can still embed custom logic directly into workflows.

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.

Vison AI

Vison AI

Hire AI Employees that deliver Human-Quality work. Automate repetitive tasks, scale effortlessly, and focus on business growth without increasing head count.

iLeap

iLeap

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

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