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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.

Advice on Airflow and Astronomer
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Apache SparkApache Spark

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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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 259.5K views
Recommends
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CassandraCassandra

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Astronomer
  • 51
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
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    Cons of Airflow
    Cons of Astronomer
    • 2
      Observability is not great when the DAGs exceed 250
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
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      What is 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.

      What is Astronomer?

      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.

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      What companies use Airflow?
      What companies use Astronomer?
      See which teams inside your own company are using Airflow or Astronomer.
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      What tools integrate with Airflow?
      What tools integrate with Astronomer?
        No integrations found

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        Blog Posts

        What are some alternatives to Airflow and Astronomer?
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        AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
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