Apache Airflow Reviews

We use this solution to monitor BD tasks.

The solution is quite configurable so it is easy to code within a configuration kind of environment. 

The ease of learning and using the solution is quite good. The learning curve is low so new users can learn in a short period of time in comparison to other products. 

The following should be improved:

• Dashboards

• Security

• Airflow web UI

• Telemetry for logging, monitoring, and alerting purposes

• Documentation 
  

I have used the solution for six months. 

The solution is 99% stable. We have a few glitches here and there but have been able to fix them. 

The solution is quite scalable. You can grow in terms of users and environment. You can grow to multi-server applications. You can use the solution on desktops, mobile, or other devices. 

We have an internal tech support team so have not needed support from the vendor. 

The setup is straightforward. The time for deployment depends on the environment and user base.

We implement the solution in-house. We have one implementation with 60 users and another with 75 users. 

We have a tech support team that consists of ten engineers who support implementations. They follow up on issues that might arise during the process automation or implementation of the workflow itself. 

For example, our tech support team will resolve a workflow that gets stuck during the MDM workflow engine. The tech team has the knowledge base to resolve any of these issues. 

The solution is open source.

I do not have exposure to use cases for large organizations with a huge user environment, so I cannot speak to the solution's effectiveness in these scenarios. 

I rate the solution an eight out of ten. 

I use this solution for scheduling purposes. We have our own Python framework to run jobs, do the extractions, and for transformation loading.

We have 20 people who are using Airflow. It's being used on a daily basis. We don't have any plans to increase usage because we have low data sets.

The solution is deployed on cloud. The cloud provider is Azure.

Everything is in the Python framework now. I would like to see some no-code capabilities and drag and drop abilities in Airflow.

We're expecting a few more improvements in the log generator. Currently, it's very clumsy.

I have used Apache Airflow for three years.

It's stable.

It's scalable. So far, we haven't needed more scalability because it's totally controlled by administrators.

The only difference between Apache Airflow and BPM software is the pricing.

Setup is about medium difficulty. You need to have some prior knowledge and experience with docker containers and AKS.

It's open-source.

I would rate this solution as seven out of ten.

We use Apache Airflow for data orchestration.

Apache Airflow is a pretty useful tool for collecting information. Apache Airflow is a pretty easy solution that can be used with Python. The solution's UI allows me to collect all the information and see the code lines.

I have some issues with the solution's communication. The solution uses the same database or data set. Sometimes, we consume the same data and send it to a different place when doing a different DAG. When using the UI, I want to see that we use the same data set more than once.

I have been using Apache Airflow for five years.

I rate Apache Airflow a seven out of ten for stability.

I rate Apache Airflow an eight out of ten for scalability. Around 400 users are using the solution in our organization.

I previously used Control-M and some AWS and Google Cloud Platform tools.

Apache Airflow's initial setup is pretty straightforward. Apache Airflow is quite intuitive to set up and create DAGs.

It takes around two days to deploy Apache Airflow. A DAG can be created in just a few hours.

Apache Airflow is deployed on-cloud in our organization.

Overall, I rate Apache Airflow a nine out of ten.

Our primary use case for the solution is setting up workflows and processes applied everywhere because most industries are based on workflows and processes. We've deployed it for all kinds of workflows within the organization.

The ability to easily set up and deploy workflows with Airflows is valuable. Additionally, designing processes and workflows is easier, and it assists in coordinating all of the different processes.

The solution can be improved by creating a tool that allows us to do these kinds of things graphically instead of just writing scripts. Hence, the graphical user interface can be improved.

We have been using the solution for approximately one year and are currently using the latest version.

The solution is stable.

The solution is scalable. Approximately hundreds of thousands of people are utilizing it.

We have not had any issues that require customer service and support.

The initial setup is intermediate, and two people are required for deployment.

There is a return on investment because it's free, open source and very useful, so there is a significant return on investment.

I rate the solution an eight out of ten.

I'm a data engineer. In the past, I used Airflow for building data pipelines and to populate data warehouses. With my current company, it's a data product or datasets that we sell to biopharma companies.

We are using those pipelines to generate those datasets.

I like the UI rework, it's much easier.

I use XCom for derived variables that need to pass between tasks. I don't really tend to use it for passing data, but only for a derived variable. For example, I don't have to re-query something every time, with one-task uses. I use the JSON comp for overwriting certain parameters.

In our use cases, some of the inputs of the dataset are files that we pulled out of S3. Sometimes they need to re-do those files, but we don't need to change any logic, we just need to redo the bills. Rather than redeploying the code to point to a new S3 bucket, we overwrite it to point to a different S3 key.

I have read that there are many different workflow pipelining tools in the biotech space, such as Snakemake and Nextflow.

There is also a CWL plugin that we may look into at some point. 

Eventually, we might have a use case where a researcher has a pipeline they run locally, and then we want to convert that to a DAG. 

The CWL-Airflow plugin would be useful for that. This might be something to look into later. But that would be like months, or maybe a year from now.

I am using a Celery Executor and I find that it crashes and I can't see any logs. I can only assume that it's a memory issue and have to blindly restart until eventually, it starts up again.

One of the use cases is triggered by input rather than a batch process. For example, we receive a batch of data, it goes through tasks one, two, and three, and a new batch comes in, each subsequent task should be operating on just that data from the prior task.

I am used to working on it as the output gets written to a table and then the next task selects all from that upstream table. It could be coded where you are only writing the data for that portion of the task. It could handle state machines and state changes as opposed to the batch proxy.

I would like to see it more friendly for other use cases.

In my current company, I just introduced it within the last couple of months. But I've used it at my prior two jobs as well.

We are using Version 2.0.1.

We are using the open-source version of Apache Airflow.

I usually create my own custom operators every time. We upgraded to 2.0, but I am not using any of the new features. 

I haven't yet used DAG of DAGs or the new way of using Python functions in the Python operator yet. But we might use DAG of DAGs eventually.

I Love this solution and I would rate it a nine out of ten.

Our primary use case for this solution is scheduling task rates. We capture the data from the SQL Server location and migrate it to the central data warehouse.

The best feature is the customization that can be done using Python. For example, there are use cases where we have to tweak the algorithm and with Apache Script Rate, we have extra functionality that helps to change the underlying process. We can define our algorithms and processes using Python.

The solution could be improved by simplifying the integration process and providing access to its support team to guide integration.

We have been using this solution for two months and it is deployed on-premises.

The solution is stable but primarily depends on the support team and how they manage it.

Apache Airflow is scalable. Approximately 20 people use this solution on my team.

We haven't had any experience with customer service and support.

Previously, we were using SQL server integration tools and integration service SSIS packages. We had project orders and wanted to migrate everything as it was an open source rate and no license was required. We switched to Apache Flow because we are trying to migrate all the projects developed in SSIS using Python.

The initial setup was straightforward. However, if a script is written, it takes four to five minutes to set up.

Apache Airflow is open source, so I cannot comment on licensing costs.

We chose this solution because it was suitable for our business needs.

I rate this solution a seven out of ten. My advice to new users is to have good proficiency with Python language. The solution is good but can be improved by simplifying its integration process.

We normally use the solution for creating a specific flow for data transformation. We have several pipelines that we use and due to the fact that they're pretty well-defined, we use it in conjunction with other tools that do the mediation portion. With Airflow, we do the processing of such data.

The product integrates well with other pipelines and solutions.

The ease of building different processes is very valuable to us. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. It's very easy to create flows. 

The graphics in the past have not been ideal.

We have several areas where we feel they could improve in terms of being a little bit more flexible. One is implementation. Even though we customized it, there were some specific things we had to do with the image by itself.

The management integration was challenging as well. It requires a lot of work on our end. We were creating our own way to integrate things specifically with specific tools. There's not really an ease of management out-of-the-box option for integration. We needed to become a little bit creative to solve that ourselves.

The scalability of the solution itself is not as we expected. Being on the cloud, it should be easy to scale, however, it's not.

There is no SDC versioning. There's no virtual control for pipelines. We have to build several pipelines for several flows, yet there's not a virtual control to generate them.

There's no Python SDK. We need to generate our own scripts and upload them and put them there. However, there's not a realistic case that we can get connected to them. On top of that, the API sets that are provided are very limited. They are not as rich as others. You cannot do much with them.

I've been using the solution for maybe three years at this point. It hasn't been too long.

The solution is largely stable. Obviously when you start creating more use cases, then you realize the limitations, however, it's not really, really bad.

Due to the fact that the solution is on the cloud, we thought it would be fairly easy to scale. This is proving not to be the case and scalability is limited.

The challenging part is to make it really flexible in a cloud-native environment. With other applications, what you have there is the scalability that can be sensitive to your needs, based on the amount of data you are putting into the flow.

Instead of you having to create your own logic to scale it up, it should be a little more efficient on how it gets integrated into the whole environment. You have to get a little bit creative and put some commands and some logic in there and be monitoring everything. You build everything - versus other options that are more out of the box. With other solutions, if you have these bursts of data they ultimately can scale up and they are more native.

Technical support has been pretty good. We don't really have anything to complain about. We're satisfied with the service so far.

For this particular category, due to the fact that we're testing all the other tools and they were too much of what we needed and due to the fact that we have used other products in other projects, and nothing really worked for us. Airflow, being a bit different, we decided that it was a nice player and a good open-source tool. 

We do use other tools. However, this one seems to work quite well for us.

The initial setup isn't as straightforward as we hoped. It's not as flexible as other options. You need to be a bit creative during the process.

This product is open-source.

We're just customers and end-users. We don't have a special business relationship with Apache.

I'm not sure of which version of the solution we're using. It's likely the most up-to-date, or at the very most back two or three versions as we are not using any of the older versions.

I'd advise others considering the solution to first understand what exactly you're trying to achieve. You either select a non-cloud native Apache workflow manager or select something that is way too big for what you are actually trying to achieve. Understand what is exactly what you need and the volumes that you need, and what exactly are the use cases.

After that, in terms of deployment, that depends on what you exactly are trying to do. If all of your solutions are cloud-native, try to do it with a cloud-native tools solution. Specifically, go to the CMCS site and look into the solutions that there. Those have been tested at least for the cloud-native solutions that exist.

Then, just make sure that the components you have will match and will be available to whatever you're trying to build. For example, the user management is something that is important for us and for this specific setup. Probably for some others, it's not going to be. 

Take into consideration, what are the different connection points and make sure that they are either supported or that you can support the integration of such items. You need to have a proper developer that can help you build your connector or your API.

In general, I would rate the solution at a seven out of ten. If they fix the APIs and the price on LTK, I'd rate it closer to a nine.