I like Apache Airflow's clear DAGs since they make workflows easy to understand and maintain. The scheduling feature ensures pipelines run automatically without manual effort, which is really helpful. I also appreciate the retries and monitoring, as they help quickly detect and recover from failures. Additionally, its scalability is a significant advantage, allowing me to handle growing data workloads reliably, making Airflow dependable for production pipelines. Overall, these features really enhance my experience with Apache Airflow. Review collected by and hosted on G2.com.

Some things in Apache Airflow not work very well for me. Setup and initial configuration is little complex and takes time. UI sometimes feels slow when lot of DAGs are running. Debugging failed tasks is not always clear, logs are scattered. Also version upgrades can break existing DAGs, backward compatibility should be better. Review collected by and hosted on G2.com.

Apache Airflow makes it incredibly easy to design, schedule, and monitor complex workflows using Python. I like how it allows building DAGs in a very readable and modular way, which helps in managing large-scale data pipelines. The UI is intuitive and gives full visibility into task execution, retries, and logs. Its ability to integrate seamlessly with databases, cloud providers, and external services makes it very flexible for real-world use cases. The community support and available plugins also make it easy to extend functionality as needed. Customer Support is also good. Review collected by and hosted on G2.com.

Airflow can be a bit challenging to set up and configure initially, especially when deploying in production with multiple workers and schedulers. Resource management and scaling sometimes require additional tuning, and debugging can be tricky for new users. The learning curve is steeper compared to some other orchestration tools, and the UI, while useful, could be more modern and responsive. However, once set up, it becomes stable and very reliable. Review collected by and hosted on G2.com.

I use Apache Airflow for project flow management and monitoring. I find its web-based UI and Python scripting features valuable, making it easy to develop and design process flows. Python as a scripting language is more user-friendly than other complex languages, which helps in writing complex flowcharts better than with traditional languages. Review collected by and hosted on G2.com.

The installation process of Apache Airflow is quite complex and highly dependent on PIP, making it very difficult to handle on cyber security blocked servers. The initial setup is challenging, especially in a proxy-based environment, as it requires lots of permissions and manual installations. Review collected by and hosted on G2.com.

What I like best about Apache Airflow is its flexibility and powerful scheduling capabilities. As a developer, I can design complex workflows as code using Python, making it easy to version-control and collaborate with teammates. The UI is intuitive for monitoring DAG runs and troubleshooting issues, and Airflow’s large ecosystem of integrations lets me connect with almost any tool or database Review collected by and hosted on G2.com.

The learning curve is pretty steep, particularly when configuring the scheduler and managing task dependencies. Sometimes Airflow’s web UI feels sluggish, and troubleshooting issues can get complicated with complex DAGs. Also, while there are a lot of integrations, keeping dependencies compatible during upgrades isn’t always smooth. Review collected by and hosted on G2.com.

Airflow enhances pipeline observability - both process and data - to the highest level.

It enables the distribution of pipeline execution among a team of stakeholders with varying technical backgrounds in a safe and user-friendly environment. Review collected by and hosted on G2.com.

The installation, setup, and running are not straightforward, and some fine-tuning is necessary. Review collected by and hosted on G2.com.

What I like best about Apache Airflow is how it lets me orchestrate complex data pipelines in a very structured way. In supply chain demand planning, we deal with multiple data sources – sales, inventory, production, even external signals like holidays or weather. Airflow makes it easier to schedule, monitor and re-run these workflows without too much manual hassle. I also like the visibility it gives through the UI, it helps to quickly catch when a task is failing and why. For me, this saves a lot of time compared to writing adhoc scripts and cron jobs. Review collected by and hosted on G2.com.

Sometimes Airflow can feel a bit heavy, specially when setting it up the first time. For smaller workflows it almost feels like an overkill, but in larger supply chain planning projects it pays off. The UI is good but can be slow when you have too many DAGs running. Also, learning curve is not trivial – it takes some time to get comfortable with operators, connections and handling backfills. I also wish the documentation had more real-world supply chain use cases instead of just generic ETL examples. Review collected by and hosted on G2.com.

Setting up ETL pipelines and orchestrating workflows is straightforward, thanks to the wide range of integrations available with nearly every data source and enterprise application. Review collected by and hosted on G2.com.

While there is a wide range of possible integrations, the built-in scheduler is not particularly advanced when it comes to managing complex scheduling requirements. Review collected by and hosted on G2.com.

Apache Airflow is very much helpful in orchestrating complex work flows. I really love the DAG based workflow orchestration, this helped me in breaking down large tasks into smaller tasks which made debugging easy. The best thing I like about airflow is its retry mechanism, If I want to run a specific task of a Dag or Dag failed at a particular task then I can just retry at the specific task instead of running entire Dag from start which really saved a lot of time. One more best thing about Airflow is its Dynamic Dag approach, When there is requirement to create multiple similar DAGs then we can create a specific template and use that template for all the similar DAGs which is really an amazing feature, this helped me a lot and reduced manual writing. I have been using Airflow for 1 Year and I feel that Airflow is the best platform for Orchestration Workflows. Customer support is very responsive and helpful. Review collected by and hosted on G2.com.

There is no proper documentation for some Operators which makes difficult for new users. Review collected by and hosted on G2.com.

Opensource, UI to track almost every aspect of each job, python friendly. Review collected by and hosted on G2.com.

While Apache Airflow is powerful, it often complicates simple tasks with added abstractions like custom directives and inter-task communication. Job scheduling isn’t intuitive—requiring attention to interval ends—and log loading can be sluggish. Though opinions may vary, I personally find traditional cronjobs a simpler and more effective solution for managing a large number of jobs. Review collected by and hosted on G2.com.

It has a nice user interface for viewing the status of DAGs, which has become an industry standard. Additionally, when jobs fail, the logs are very helpful for tracking down what went wrong. Review collected by and hosted on G2.com.

The setups feel outdated and unnecessarily complex. In comparison, tools such as dbt, and especially Databricks, have made significant improvements recently. Review collected by and hosted on G2.com.