Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
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Cisco TrustSec
Score 5.0 out of 10
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OpenText Operations Orchestration
Score 10.0 out of 10
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OpenText™ Operations Orchestration automates, integrates, and orchestrates any IT process, on cloud or off. Automations use low-code/no-code workflow authoring options. Integrations are done with an API-rich, extensible platform. Centrally orchestrates workflows.
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Pricing
Apache Airflow
Cisco TrustSec
OpenText Operations Orchestration
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Apache Airflow
Cisco TrustSec
OpenText Operations Orchestration
Free Trial
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Free/Freemium Version
Yes
No
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
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No setup fee
Additional Details
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Community Pulse
Apache Airflow
Cisco TrustSec
OpenText Operations Orchestration
Considered Multiple Products
Apache Airflow
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Anonymous
Chose Apache Airflow
Step functions are only available in AWS but Apache Airflow provides cross cloud access. Apache Airflow also provides flexibility to pause, start and re-trigger dags. Provides executors where we can run in-house calculations if needed and which requires no integration with …
Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of …
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the …
Using Jenkins and Kafka, it is not for the same purpose, although it might be similar. I would say AirFlow is really what it says on the can - workflow management. For our organisation, the purpose is clear. So long your aim is to have a rich workflow scheduler and job …
Much easy to deploy Apache Airflow as opposed to other products, with flexible deployment options as well as flexible integration with other tools and platforms.
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a …
digdag (https://www.digdag.io/)- Digdag is a very simple build, run, schedule, and monitor complex pipelines of tasks with a simple implementation and no configuration. Easy to write YAMLs
Airflow has a better community and widely adopted. Has a better UI and better documentation
Overall using Apache Airflow is easy to use compare than other other tools available in the market, It is easy to integrate in apache airflow and the workflow can be monitored and scheduling can be done easily using apache airflow, recommend this tool for Automating the data …
Most product works well enough, but none of them combine the non-agent/client approach to automation with an easy to develop mechanism (the drag&drop development approach) This alone put this solution on top of our preferred implantation product list
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
Control access to critical enterprise resources by business role, device type, and location, so policy changes can be made without redesigning the network.
Easily manage access control and segmentation while maintaining compliance.
Create and manage policies in an easy-to-use matrix.
Reduce the need for costly network re-architecture by automating firewall rules and access control list (ACL) administration.
For our business, this system is really THE system. We use it for the management of almost all aspects of a member's account. I honestly cannot think of something that it cannot do.
Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.
Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.
Most product works well enough, but none of them combine the non-agent/client approach to automation with an easy to develop mechanism (the drag&drop development approach) This alone put this solution on top of our preferred implantation product list