Apache Airflow Reviews & Product Details

What I like most about Airflow is its flexibility and number of features for building workflows using DAGs. It is very useful for managing complex pipelines with dependencies. Integration with different systems is also strong. Once setup is done, it works reliably and is used frequently in day-to-day operations. Review collected by and hosted on G2.com.

Ease of use is one area where it can improve, especially for new users. Initial setup and implementation take effort if you are managing it yourself. The UI is not very user-friendly and sometimes slow. Also, debugging failed workflows can take time. Review collected by and hosted on G2.com.

What I like most about Apache Airflow is how it makes managing workflows feel organised and predictable. You can clearly define tasks, set dependencies, and see everything laid out in one place, which makes complex pipelines much easier to understand.

I also like how flexible it is. You can build and customise workflows to fit pretty much any data pipeline, and the scheduling just works once it is set up. The UI is another big plus, it makes it easy to monitor runs, debug issues, and quickly see where something has failed without digging through logs for ages. Review collected by and hosted on G2.com.

One thing I don’t like about Apache Airflow is that it can feel quite heavy and complex, especially at the start. Setting it up properly takes time, and there are quite a few moving parts to understand before everything runs smoothly.

Debugging can also be frustrating at times. When something fails, the logs are not always the easiest to follow, so it can take longer than expected to figure out what went wrong. It can also be a bit resource intensive, which is not ideal if you are running smaller projects or just need something lightweight. Review collected by and hosted on G2.com.

For me, the standout feature is definitely the Web UI. As a data engineer, I often find myself troubleshooting, and the Grid view in Airflow makes it remarkably simple to identify exactly where a pipeline has failed. I can quickly access the logs for any specific task and determine what went wrong within seconds. This level of transparency is something that traditional cron jobs or basic scripts simply don't offer. Having a central dashboard for all your workflows truly provides peace of mind. Review collected by and hosted on G2.com.

The main challenge is the ease of implementation for beginners. Setting up the infrastructure (like the webserver, scheduler, and database) requires a good bit of DevOps knowledge, which can be a hurdle for a small team.

Because it’s open-source, you don’t have traditional customer support, so you rely heavily on the community. While the community is active, the documentation can sometimes be a bit overwhelming when you are trying to troubleshoot a very specific configuration issue. It’s a powerful tool, but the ease of use from a setup perspective could definitely be improved. Review collected by and hosted on G2.com.

the whole process is very extensible, since there are so many users, we have a lot of built in operators/plugins. the UI is very clean and intuitive. Review collected by and hosted on G2.com.

i dont think there is a feature where we can watch the logs of all the jobs in one place, lets say we have 20-30 steps, so we have to go to each step and check the logs, so i would all logs to be consolidated Review collected by and hosted on G2.com.

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.