Often, someone else would have already asked that question, and they would have already got the answer, and you can just look it up.ĭevelopment on Apache Airflow is really fast, and it's easy to use with the newer updates. It has large communities, so if you need something or you want to ask something, you can. You have a lot of documentation and a lot of people helping out. The most valuable feature is that it's the most popular data orchestration tool in the market right now.
Apache airflow alternatives software#
This makes the programmatic aspect of our work easy for us, and it means we can automate a lot.”Ī Senior Software Engineer at a pharma/biotech company comments that he likes Apache Airflow because it is “Feature rich, open-source, and good for building data pipelines.”Īpache Airflow was previously known as Airflow. 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.
Apache airflow alternatives code#
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.
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