Need advice about which tool to choose?Ask the StackShare community!
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.
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.
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.
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.
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.
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.
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.
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.
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
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Astronomer
Sign up to add or upvote prosMake informed product decisions
Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Astronomer
Sign up to add or upvote consMake informed product decisions
What is Airflow?
What is Astronomer?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Astronomer?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Airflow?
What tools integrate with Astronomer?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+17