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  5. Airflow vs Apache Camel

Airflow vs Apache Camel

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Apache Camel
Apache Camel
Stacks8.2K
Followers323
Votes22
GitHub Stars6.0K
Forks5.1K

Airflow vs Apache Camel: What are the differences?

Key Differences between Airflow and Apache Camel

Airflow and Apache Camel are two popular frameworks used for building and managing data pipelines and integrating systems. While they serve similar purposes, there are several key differences between the two.

  1. Technology Stack: Airflow is built primarily using Python and leverages its rich ecosystem of libraries and tools. On the other hand, Apache Camel is written in Java and utilizes its extensive support for enterprise integration patterns.

  2. Workflow Orchestration vs. Integration Framework: Airflow is primarily a workflow orchestration tool that focuses on managing and scheduling workflows as a series of tasks. Apache Camel, on the other hand, is an integration framework that enables message routing, transformation, and integration between various systems.

  3. Data Processing Paradigm: Airflow follows a batch processing paradigm, where tasks are executed in predefined intervals or upon event triggers. Apache Camel, on the other hand, supports both batch processing and real-time event-driven processing, making it suitable for a wider range of use cases.

  4. Flexibility vs. Convention: Airflow provides a high degree of flexibility in designing workflows and allows developers to define custom operators and hooks. Apache Camel, on the other hand, follows a convention-over-configuration approach, providing a set of predefined integration patterns and components for developers to use.

  5. Community and Ecosystem: Airflow has a large and active community of users and contributors, resulting in a wide range of connectors, plugins, and integrations available. Apache Camel also has a vibrant community but focuses more on enterprise integration patterns and has a smaller ecosystem compared to Airflow.

  6. Scalability and Deployment: Airflow is designed to scale horizontally and can handle large-scale data pipelines with distributed execution across multiple worker nodes. Apache Camel is also scalable, but its deployment model is typically based on Java application servers, which may have different considerations for scalability and resource management.

In summary, while both Airflow and Apache Camel are powerful frameworks for building data pipelines and integrating systems, Airflow focuses more on workflow orchestration, provides flexibility in workflow design, and has a larger community and ecosystem, while Apache Camel is a feature-rich integration framework with extensive Java integration capabilities and support for real-time event-driven processing.

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Advice on Airflow, Apache Camel

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

Airflow
Airflow
Apache Camel
Apache Camel

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.

An open source Java framework that focuses on making integration easier and more accessible to developers.

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
GitHub Stars
-
GitHub Stars
6.0K
GitHub Forks
-
GitHub Forks
5.1K
Stacks
1.7K
Stacks
8.2K
Followers
2.8K
Followers
323
Votes
128
Votes
22
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Open source - provides minimum or no support
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
Pros
  • 5
    Based on Enterprise Integration Patterns
  • 4
    Highly configurable
  • 4
    Has over 250 components
  • 4
    Free (open source)
  • 3
    Open Source
Integrations
No integrations available
Spring Boot
Spring Boot

What are some alternatives to Airflow, Apache Camel?

Heroku

Heroku

Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling.

Clever Cloud

Clever Cloud

Clever Cloud is a polyglot cloud application platform. The service helps developers to build applications with many languages and services, with auto-scaling features and a true pay-as-you-go pricing model.

Google App Engine

Google App Engine

Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.

Red Hat OpenShift

Red Hat OpenShift

OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.

AWS Elastic Beanstalk

AWS Elastic Beanstalk

Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.

Render

Render

Render is a unified platform to build and run all your apps and websites with free SSL, a global CDN, private networks and auto deploys from Git.

Hasura

Hasura

An open source GraphQL engine that deploys instant, realtime GraphQL APIs on any Postgres database.

Cloud 66

Cloud 66

Cloud 66 gives you everything you need to build, deploy and maintain your applications on any cloud, without the headache of dealing with "server stuff". Frameworks: Ruby on Rails, Node.js, Jamstack, Laravel, GoLang, and more.

Jelastic

Jelastic

Jelastic is a Multi-Cloud DevOps PaaS for ISVs, telcos, service providers and enterprises needing to speed up development, reduce cost of IT infrastructure, improve uptime and security.

Dokku

Dokku

It is an extensible, open source Platform as a Service that runs on a single server of your choice. It helps you build and manage the lifecycle of applications from building to scaling.

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