Apache Airflow Reviews

We use the product to orchestrate data engines and process new data files.

The product's most valuable feature is scalability. It helps us run hundreds of data jobs every day.

The platform's stability needs improvement, particularly regarding occasional interruptions due to networking issues. It requires manual intervention to resume jobs. Additionally, while extending the code is possible, it sometimes necessitates creating custom plugins.

We have been using Apache Airflow for four years.

We have more than 100 Apache Airflow users in our organization.

The initial setup on Google Cloud using Cloud Composer is straightforward and simplified. However, deploying it on-premises can be complex and challenging.

The product is worth the investment.

It is an open-source solution, so there are no hidden fees or licensing costs associated with the software. However, users need to cover the operational costs for the actual infrastructure, such as the virtual machines (VMs).

The directed acyclic graph (DAG) functionality in Apache Airflow has significantly enhanced our workflow management. It provides a visual representation of data processing tasks.

The user interface for monitoring and managing workflows has been excellent, particularly in the latest version. It is difficult for beginners to use the platform, and some training is required.

I recommend the product to others, and it is much better than our competitors. It is an open source. I rate it a seven out of ten.

We use Apache Airflow for the automation and orchestration of model deployment, training, and feature engineering steps. It is a model lifecycle management tool.

We have an integration with Apache Airflow in our portal for messaging. We use group and transformation data from Redshift to Tesco, and then create a call flow to the router. This is a source of data leakage, such as data engineering and machine learning, especially in a HIPAA environment. We need to check the evolution steps in the pipeline. In production, we only have two cases. Sometimes, we need customer data not in the database, which we get from object storage. The call flow from Redshift to Tesco involves transforming the data and then generating it with the router or Kibana router for the policy. The data is then transformed and sent to the dashboard or data warehouse.

Airflow is a pipeline for transferring code by clients, but for experimental model experiments, Apache Airflow does not have any solution. There is a need for more features on experimental evolution steps.

I have been using Apache Airflow for one and a half years.

The product is stable. I rate the solution’s stability an eight out of ten.

20 users are using this solution in our organization. I rate the solution’s scalability an eight out of ten.

The initial setup is not complex and can be done by two people. However, open-source prime solutions have some difficulties. We can schedule Apache Airflow on Kubernetes. Space limitations and installation issues may arise, as we do not have full control over Kubernetes cluster resources, and our administration is limited. I rate the initial setup a six out of ten, where one is difficult, and ten is easy.

I recommend Apache Airflow because it is still profitable and can be used with multiple systems and servers, Kubernetes systems, and dashboard systems. You can use it to get social media and other data, but it can be expensive. Overall, I rate the solution a nine out of ten.

My team works on commerce services. We use Airflow to synchronize user information or product information from other services. We use the tool for automating data pipelines. We store user history about API calls and show it on a statistics page, like daily or real-time statistics. We use the solution to aggregate API user's data.

Kubernetes from the batch application is the most useful to my team. It uses Python. It is simple. There are not many learning costs. We're using the scheduler. We don't need to care about the batch job every day. We just need to notice when the alerts are firing. It is convenient for us. The product supports many other services, like Kubernetes. I saw some custom applications and programs. The solution integrates very well with other products.

The documents do not precisely define the function of the operators. I had to do some experiments to understand the function of the operators. The documentation must be improved. Some parts of the documentation do not precisely explain the parameters and functions. We often need to do experiments to understand how they work.

I have been using the solution for one and a half years.

I rate the tool’s stability a nine out of ten.

I rate the tool’s scalability a six or seven out of ten. We haven’t horizontally scaled the solution. At least 20% of the teams in my organization are using Airflow to do some batch jobs. There are around 300 users.

I rate the ease of setup an eight out of ten. The product is deployed on the cloud. We release Airflow on Kubernetes. The deployment takes less than five minutes. We use a deployment tool made by our company to deploy the solution.

I am also using Apache Kafka.

I will recommend the product to others. The UI is very simple and easy to learn. There are a lot of users of the product. We can find information easily on Google. Overall, I rate the tool an eight out of ten.

Apache Airflow is useful for workflow automation, making it capable of automating pipelines, data pipelines, and data warehouse processes. I don't have a strong need for Apache Airflow because I do everything with a dbt or data build tool since it has its own integrated workflow process.

I use Fivetran to synchronize my data. I don't need to do any automation on that and don't have any need for workflow automation. I have everything I need.

We were experimenting with the solution. We never reached the point where we would deploy the solution in the production capacity.

The problem with Apache Airflow is that it is an open-source tool. You have to build it into a Kubernetes container, which is not easy to maintain, and I find it to be very clunky.

Additionally, there is room for improvement with DAGs. I had a very hard time building DAGs in Apache Airflow. I decided to use Astronomer, which is on top of Apache Airflow and is supposed to make your life easier. The best part of the solution is the third-party add-on which is Astronomer.

It would be a very nice tool if it could have been an entirely cloud-based solution. Apache Airflow is not so nice when you have a hybrid setup, such as half is on-premises and half of it is on a cloud environment. It should integrate better with the outside world.

I have been using Apache Airflow for a couple of months.

I have no opinion on the solution's stability. The solution did not get to a production capacity. I couldn't even do file processing with Apache Airflow. None of the engineers could actually help me set up Apache Airflow. I had to give up on the product. Just buy a product that works, and you will be done with it.

The initial setup was complex to deploy on the cloud. Installing the software is very difficult. The documentation is very bad. There is no installer where you can press a button, and it does everything for you. One may need a couple of engineers to install the solution, which is an issue with open-source tools. Price-wise, the software falls on the cheaper side. With Apache Airflow, one may spend much more on engineers.

The solution is deployed purely on the cloud.

I didn't experience any ROI using the solution. I could do everything without Apache Airflow since it would have been just a money pit.

I suggest others not use Apache Airflow. If you use Apache Airflow, you will waste your time unless you have a bunch of engineers who already know about the solution.

If you cannot write a DAG within two hours of starting the process, then forget about the tool, and it would be better if you tried to find something else.

Overall, if the tool was working properly, it would be very good, but unfortunately, it is not.

Overall, I rate the solution a five out of ten.

We use Apache Airflow to send our data to a third-party system.

We are already on Python. Since Apache works very well on Python, we can manage everything and create pipelines there.

Adding more automated components in Apache Airflow for basic things like exporting the data would be helpful. Apache Airflow is not that easy to use, but we have gotten used to it.

I have been using Apache Airflow for three years.

Apache Airflow is a stable solution.

Apache Airflow is not a scalable solution for our use cases. We have a very huge list of use cases. Over 10 developers use Apache Airflow in our organization.

Apache Airflow's technical support team is good and provides assistance almost 90% of the time.

Apache Airflow's initial setup is easy. It's not that difficult, but it has a learning curve.

Apache Airflow is a cheap solution.

Depending on your use case, if you are looking for a quick solution to work on and know Python, you should go ahead with Apache Airflow.

Apache Airflow is a good enough tool for managing data pipelines. However, the solution is not up to the mark as you scale up and go at the higher performance. Apache Airflow has introduced the DAG connector for managing data pipelines.

Overall, I rate Apache Airflow an eight out of ten.

Apache Airflow is utilized for automating data engineering tasks. When creating a sequence of tasks, Airflow can assist in automating them.

The most valuable feature of Apache Airflow is creating and scheduling jobs. Additionally, the reattempt at failed jobs is useful.

We have faced scenarios where Apache Airflow becomes non-responsive, leading to job failures. To resolve such situations, we had to manually reboot Apache Airflow since it doesn't provide an option to restart within the application. This necessitated modifying some configurations to initiate a restart of all Apache Airflow components. Although Apache Airflow is generally dependable, it may occasionally encounter glitches that can disrupt production flows and batches.

I have been using Apache Airflow for approximately three years.

We experienced some glitches using the solution with some errors.

I rate the stability of Apache Airflow a five out of ten.

Apache Airflow is scalable because it is within Amazon AWS.

I rate the scalability of Apache Airflow an eight out of ten.

The technical support is good, they are able to debug issues.

The initial setup of Apache Airflow was simple because it was all managed by Amazon AWS. The process took a few minutes.

The solution is free if you use Amazon AWS.

I would recommend this solution for projects even though there have been glitches. Once the solution has become stable it would be ideal for critical projects.

I rate Apache Airflow a seven out of ten.

This is great software to build data pipelines. However, we had many glitches that were causing some problems in production.

We use Apache Airflow for the orchestration of data pipelines.

Since it's widely adopted by the community, Apache Airflow is a user-friendly solution.

Apache Airflow should have better integration with cloud platforms.

I have been using Apache Airflow for a couple of years.

Apache Airflow is not a stable solution.

Around ten people are using the solution in our organization.

The solution's initial setup is difficult and should be done by an experienced person.

Apache Airflow is a cheap solution.

The solution is deployed on the cloud in our organization. Before choosing Apache Airflow, users should try cloud-native services first.

Overall, I rate the solution a seven out of ten.

We were using Apache Airflow for our orchestration needs. We used it for all the jobs that we had created in Databricks, Fivetran, or dbt. These were the three primary tools that we were using. There were a few others, but these were the three primary tools. So, Apache Airflow was for the job orchestration and connecting them to each other for building our entire data pipeline. We were also using Apache Airflow for dbt CI/CD purposes.

The most valuable feature is that it's the most popular data orchestration tool in the market right now. It connects to everything you need.

It's open-source. You have a lot of documentation and a lot of people helping out. It has large communities, so if you need something or you want to ask something, you can. Often, someone else would have already asked that question, and they would have already got the answer, and you can just look it up.

Development on Apache Airflow is really fast, and it's easy to use with the newer updates. Everything is in Python, so it's not hard to understand. They also have a graphical view, so if you are not a programmer and you are just an administrator, you can easily track everything and see if everything is working or not. For notifications, it can connect with different messaging tools such as Slack and Teams, as well as with webhooks. It's very easy to use, and it has a lot of features that you would expect from any of the data orchestration tools.

Programmatically, it's very good, and it doesn't have any competitors, but you cannot develop anything in Airflow UI. You need to develop everything within the program. 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. All the DAGs you want to build need to be coded in Python. It doesn't provide features for graphical programming. You cannot drag and drop something, build a pipeline out of that, or orchestrate that with a drag and drop. They have a graphical feature but only for administration purposes, not for development. They don't have a UI for development.

It doesn't support the Windows system. That's a big drawback because a lot of people are using Windows. 

I used Apache Airflow on my previous project. We had planned to use it in our current project, but due to time issues, we were not able to deploy it. In my previous project, I used it for around eight or nine months.

It's a very stable product.

It's highly scalable. You can scale it as much as you want. It depends on the size, and you need to scale up your instance. We had over 3,000 DAGs in our previous project, and we didn't face any issue with even 8 GB memory in our EC2 instance. If you have a lot of DAGs, you might need to scale up, but it's quite lightweight, so you don't need to worry much about that.

It's open source. It was my first project, and I had a few doubts, but everything I needed was available on the internet, so I never had to contact their support. I might have been able to post my questions on their GitHub, but I didn't need that. Airflow has a very large community, so any questions you ask get answered there.

Its setup wasn't done by us. It was done by the Astronomer team on Azure Community Services. So, it was deployed and set up on Azure Community Service. Everything was taken care of by the Astronomer team.

Apache Airflow has two large and popular distributors. There might be others, but the two popular ones are Bitnami and Astronomer. For us, everything was set up by Astronomer.

It's open source. You can install it locally on your own system. If you are deploying it in the production system, you normally deploy it on some cloud, such as EC2 service, which would have some cost. If you are setting up a Docker container or something for Apache Airflow yourself, which is quite easy, you can do pretty much everything online. I have set it up on my local system, and It doesn't take a long time. You can do customization for your project such as selecting different repository databases or selecting different cellular or web services, which is good.

If you are going with a service provider such as Astronomer or Bitnami, they will charge you because they are a distributor of Airflow. They have some of their own features and their own support. They will charge you if you are going with them.

If you are on a Mac or Linux system, it's very easy to install. You can just go to the Apache website to install it, and you can start working, but Apache Airflow doesn't support Windows Exe installation, so if you have some knowledge of Docker containers for WSL, it'll be useful.

Other than that, Astronomer has an instructor called Marc Lamberti who is very popular in the Airflow community. He has YouTube videos. In five minutes, he can teach you how to set up Airflow or what DAGs are. He has five or six videos, and he gets into the details with his videos. So, if you have no idea about Apache Airflow and you don't want to go through all the documentation, you can start with those videos, but if you have a Mac or Linux system, you can directly install it on your system.

I'd rate it a seven out of ten because it doesn't support Windows, and it doesn't support graphical designing, so we cannot create DAGs in the UI. We can administer and look at DAGs through the UI, but we cannot create DAGs through the UI. Other orchestration tools that are available in the market provide that feature.