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

Airflow vs Astronomer

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Astronomer
Astronomer
Stacks26
Followers47
Votes0

Airflow vs Astronomer: What are the differences?

Introduction

Airflow and Astronomer are both popular tools used for orchestrating and managing workflows. While they share similarities in their core functionality, there are several key differences that set them apart. This Markdown code provides a brief comparison between Airflow and Astronomer.

  1. Built-in Features: Airflow is an open-source tool that offers a wide range of built-in features such as task dependencies, scheduling, and monitoring. On the other hand, Astronomer is a platform that utilizes Airflow as its core engine, but also provides additional features like managed deployment, enterprise-grade security, and integration with cloud providers.

  2. Hosting Options: Airflow can be hosted on any infrastructure, allowing users to choose their preferred hosting environment. Astronomer, on the other hand, provides a managed platform-as-a-service (PaaS) option. This means that Astronomer takes care of the infrastructure and hosting, making it more convenient for users who prefer a fully managed solution.

  3. Ease of Deployment: Airflow requires a manual installation and setup process, which may involve configuring dependencies and managing server infrastructure. Astronomer simplifies the deployment process by providing a user-friendly interface and automated infrastructure provisioning, allowing users to easily set up and deploy Airflow workflows without the need for complex configuration.

  4. Enterprise Support: Astronomer offers enterprise-level support, which includes dedicated customer support, service-level agreements (SLAs), and the ability to handle larger-scale deployments. Airflow, being an open-source tool, does not provide the same level of official support as Astronomer. However, there is an active community that can provide support and assistance.

  5. Integration with Diverse Range of Tools: Airflow supports a wide range of integrations with other tools and technologies, including databases, cloud providers, messaging systems, and more. Astronomer inherits this compatibility and extends it further by offering integrations with popular cloud providers and data sources. This allows users to seamlessly incorporate their existing tools and services into their workflows.

  6. Pricing Model: Airflow is an open-source tool and does not require any licensing fees. Astronomer, on the other hand, follows a subscription-based pricing model, where users pay for the managed platform and additional enterprise features. The pricing structure may vary based on factors such as usage, scale, and additional requirements.

In summary, Airflow is a powerful open-source tool with a strong community support, while Astronomer provides a managed platform-as-a-service solution with additional enterprise features and support. Both tools offer extensive capabilities for workflow orchestration, but vary in terms of deployment options, ease of setup, support, integration options, and pricing models.

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Advice on Airflow, Astronomer

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
Astronomer
Astronomer

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.

Astro is the modern data orchestration platform, powered by Apache Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code.

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.
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Statistics
Stacks
1.7K
Stacks
26
Followers
2.8K
Followers
47
Votes
128
Votes
0
Pros & Cons
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
No community feedback yet

What are some alternatives to Airflow, Astronomer?

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GitHub Actions

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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

Surmado Scout

Surmado Scout

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Page d'accueil

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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.

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