![]() It's very easy to create flows.”Īn Assistant Manager at a comms service provider mentions, “The best part of Airflow is its direct support for Python, especially because Python is so important for data science, engineering, and design. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. The ease of building different processes is very valuable to us. Several data source connections: Apache Airflow can connect to a variety of data sources, including APIs, databases, data warehouses, and more.īelow are some reviews and helpful feedback written by PeerSpot users currently using the Apache Airflow solution.Ī Senior Solutions Architect/Software Architect says, “The product integrates well with other pipelines and solutions.Multiple deployment options: With Apache Airflow, you have several options for deployment, including self-service, open source, or a managed service. ![]() At runtime, a context variable is passed to each workflow execution, which is quickly incorporated into an SQL statement that includes the run ID, execution date, and last and next run times. Flexibility: Apache Airflow provides you with several ways to make DAG objects more flexible.Visual DAGs: Apache Airflow’s visual DAGs provide data lineage, which facilitates debugging of data flows and also aids in auditing and data governance.Easy integration: Apache Airflow can easily be integrated with cloud platforms (Google, AWS, Azure, etc).Intuitive user interface: The Apache Airflow user interface enables you to visualize pipelines running in production, monitor progress, and also troubleshoot issues when needed.User friendly: Using Apache Airflow requires minimal python knowledge to get started.Some of the biggest advantages the solution offers include: There are many benefits to implementing Apache Airflow. Plug-and-play operators: With Apache Airflow, you can choose from several plug-and-play operators that are ready to execute your tasks on many third-party services.Scalability: Because Apache Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers, you can easily scale it.Dockerfile: By using Apache Airflow’s dockerfile feature, you can run your business’s Airflow code without having to document and automate the process of running Airflow on a server.The smart sensors are executed in bundles, and therefore consume fewer resources. Smart sensor: In Apache Airflow, tasks are executed sequentially. ![]() By using Apache Airflow, you can orchestrate data pipelines over object stores and data warehouses, run workflows that are not data-related, and can also create and manage scripted data pipelines as code (Python).Īpache Airflow has many valuable key features. The solution makes it possible for you to manage your data pipelines by authoring workflows as directed acyclic graphs (DAGs) of tasks. You have to build it into a Kubernetes container, which is not easy to maintain, and I find it to be very clunky." "The dashboards could be enhanced."Īpache Airflow is an open-source workflow management system (WMS) that is primarily used to programmatically author, orchestrate, schedule, and monitor data pipelines as well as workflows. All the DAGs you want to build need to be coded in Python." "The problem with Apache Airflow is that it is an open-source tool. ![]() In the market, other tools have come up recently as competitors to Airflow, and they also give graphical programming options, whereas Airflow doesn't provide that feature currently. You need to develop everything within the program. "Adding more automated components in Apache Airflow for basic things like exporting the data would be helpful." "Apache Airflow could be improved with the addition of more frameworks." "The solution could be improved by simplifying the integration process." "I would like to see some no-code capabilities and drag and drop abilities in Airflow." "Apache Airflow could be improved by integrating some versioning principles." "Programmatically, it's very good, and it doesn't have any competitors, but you cannot develop anything in Airflow UI. ![]()
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