Astro by Astronomer Reviews

Our main use case for Astro by Astronomer involves data pipelines and general pipelines, integrating with AWS for different use cases across teams like analytics, data science, and many other different use cases.

In my experience, the best feature that Astro by Astronomer offers is multi-tenancy, which is really important for us. You can also have a cluster with shared resources, so depending on the DAGs running, you can share the same resources and save money. Moreover, it provides a better user experience with local testing and local development, and it resolves some bugs we had with Airflow managed by AWS, where some variables and UI variables are not persisted in isolated environments and virtual environments. Only local development or local deployment works.

Astro by Astronomer has positively impacted our organization by simplifying development, and the multi-tenancy also simplified the user experience. However, I note that the cost is really significant, so this should also be taken into account.

When I say it simplified development and user experience, I mean that it definitely made onboarding easier and reduced time significantly. It also helped people understand the lifecycle, especially teams that are not tech-oriented.

Multi-tenancy has helped our team significantly because we want to share the same instance with different teams or different people who want to have access to the same cluster, which enhances user experience. It is highly recommended for organizations of a big scale.

We want to explore how to deal with multi-tenancy while using Astro by Astronomer, as it is something really important for us because it enhances user experience. Additionally, we aim to resolve some bugs that Airflow has in managed AWS, such as isolated environments, and in general, we want to optimize DevOps around local instances, which is really something cool that Astro by Astronomer provides.

I think Astro by Astronomer could improve its pricing, as it is really expensive, especially if you want to have large use in a large-scale enterprise edition, which requires you to create a VPC hosted instance that significantly increases expenses. I believe they should work on this to provide more affordable solutions.

I chose 8 out of 10 because there are deployment issues, at least initially, regarding VPC hosting and many other networking issues. Second is pricing, which is really expensive even just to start experimenting, as they also provide you a developer package for this. The total plan needs improvement, but other than that, it is acceptable.

I have been using Astro by Astronomer for just a few months after completing a POC, although I am also using Cosmos by Astronomer.

In my experience, Astro by Astronomer has been stable with no issues regarding reliability, even though we did not test it in a very large production scale.

Astro by Astronomer's scalability is good, as it handles growing workloads well with a cluster underneath, so it is scalable.

We have a team dedicated to us, so customer support with Astro by Astronomer has been great.

Previously, we used Airflow managed by AWS before switching to Astro by Astronomer.

We did not purchase Astro by Astronomer through the AWS Marketplace; we came in contact with them directly for an offer.

My experience with pricing, setup cost, and licensing has been that it is too much and not very flexible.

Astro by Astronomer also has an AI editor, but using all these agents right now does not make any significant impact. It is a nice feature to have but not important.

I do not have much experience with Astro by Astronomer's AI capabilities, but regarding governance and security, I think you can easily find these in some other agents, so I am not sure.

I find Astro by Astronomer's AI capabilities to be still pretty basic, and for complex tasks, they need work. However, the user has to be very specific about what they need, creating a trade-off between describing exactly what you want and getting exactly what you need.

I advise others looking into using Astro by Astronomer to have a POC first. I rated this product 8 out of 10.

I tried Astro by Astronomer on my personal project. I attempted to rebuild some workflow, data, and data ingestion that I used to do at my previous job with Astro by Astronomer. I also tested Astro by Astronomer against other AI tools such as Gemini and Copilot to evaluate how Astro by Astronomer could create a folder structure for my project, create files, arrange configuration files, and create three folders.

My main goal was to create some custom operators or to make suggestions on existing operators that are already deployed on GitHub.

I think the possibility to share the same project and to have a central point where to develop with Astro by Astronomer is incredibly valuable, especially when sharing with other colleagues. I found that the main feature I really loved, which I also found only on Claude, is the ability to create the DAG in the best way and to understand the context.

It was very useful for my workflow management with Astro by Astronomer because when you share the same project with other colleagues, you need to keep track of every change that you make on the code.

I think the possibility to save time developing DAG in a smarter way with Astro by Astronomer is a significant benefit because, as I mentioned before, it suggests the best operator for your use case. You only need to define the right context and the right goal, and it will provide the best operators to combine with each other to create a real pipeline.

Last year, as I shared with the product manager, I thought that it would be great to have a slower response time with Astro by Astronomer. I used to wait a few minutes to get a response and to see that the DAG was created completely, which I think is annoying when you work and need to wait until the suggestions were made after a few minutes. The speed, I think, was the part to improve, but the quality, for instance, was amazing.

I think that one year ago, Astro by Astronomer needed to improve in reliability because it was a bit slow to develop and sometimes it crashed. As I said, it was before the launching date, so if they give me the opportunity to try it again, I will do that and test again. But the pain points were there.

I tried Astro by Astronomer for one month last year since I was part of the championship program, so I tested it before it was launched on the market.

I have never tried the technical support of Astro by Astronomer in terms of contacting them directly, but when I reported those problems to them, they were very supportive with me, especially because they were about launching that product. They were at the beginning of the journey and were very open to receiving any type of feedback. I would rate Astro by Astronomer's technical support as an eight.

I decided to switch to Astro by Astronomer from the solutions I used before because I know that Astro by Astronomer was developed by Astronomer, who also maintains the Airflow GitHub repo. I think that when you rely on a company that provides the technical support for a tool, you can have the full package. I would use it during my next company because I think it is a real game changer in developing pipelines.

One year ago when I tried Astro by Astronomer, I only needed to log into the platform, which was used on the website, so it was not locally developed.

I have not evaluated other options before choosing Astro by Astronomer.

For me, it was how Astro by Astronomer was able to create a deep tree folder structure because when I used to create some DAG with Gemini and with Copilot, they were not able to go deeper on how configuration files could be created and what type of operator they should use. With Astro by Astronomer, it was able to create the right path for every file and also to use the best operator based on the version that I provided.

I used to work with Copilot integrating in Visual Studio Code, and I used to work with Gemini as well before using Astro by Astronomer.

Also, because in the last month I tried Claude and I love using Claude code integrating with Visual Studio Code. I think that if they developed something very similar that you can integrate into your IDE, it would be a huge jump into the competition with Claude and other AI companies.

My main use case for Astro by Astronomer is to proof out concepts of functionality and learn about the behind the scenes for the application. I'm not an admin on our company's instance, so my vantage point is limited. This helps me get a peek at what's behind the scenes and make better decisions based on what I've learned from that.

I use Astro by Astronomer to test out determining concurrency limits and how to set them and do load balancing. I also can use it to test out different ideas for use cases and after I have given them a good test, I can possibly fold them into my work experience or personal, or I can keep them as personal projects on the side to keep exploring.

I feel the service you provide is fairly easy to set up, much easier than the first time I had to create my own local Airflow instance, so that was much appreciated.

Once you get going, it's pretty easy to use. The best features Astro by Astronomer offers, in my opinion, are easy setup and good tutorials. The courses that Astronomer provides for learning are very useful as well.

I believe your instance has a lot of really good integration with other services, so that helps with keeping it in mind whenever I am trying out different things involving different tools and how they communicate.

The impact of Astro by Astronomer on my organization is mainly personal. Our company uses an Amazon managed Airflow, so we don't use Astro by Astronomer in a production sense, and it's mainly confined to my own personal use cases. Astro by Astronomer helps me in skill development and learning mostly.

I think any further refinements as far as anything major is going to be on the user's side. Everybody's got a different need, so trying to build an overfit solution is only going to hurt other people.

The reason I choose eight for Astro by Astronomer is that it is useful for learning and for companies, small to large. There is still a barrier for people who are not familiar with some of the prerequisite knowledge, but I do also think that is not something that needs to be fixed. It is just the nature of how this works. As I said before, if you try to tailor it too much where people were getting almost a no-code experience with it, I think due to the nature of the services it provides, it would cause more trouble than it would fix. An eight means you're doing great.

I use Astro by Astronomer every now and then, probably a couple times a year.

In my experience, Astro by Astronomer appears to be stable.

I originally set everything up using the actual Airflow documentation, and it was a lot more difficult, so Astro by Astronomer made it a lot simpler.

I feel the service you provide is fairly easy to set up, much easier than the first time I had to create my own local Airflow instance, so that was much appreciated.

I have had no experience with pricing, setup cost, and licensing.

I did not evaluate other options before choosing Astro by Astronomer. This was the most prominent suggestion.

I haven't explored the governance and security facet of Astro by Astronomer's AI capabilities thoroughly, so I couldn't speak on it.

I don't use the AI component of Astro by Astronomer, so I can't comment on its accuracy and reliability of output.

Astro by Astronomer is not deployed by my administration. It is a local instance that I have used.

My advice to others looking into using Astro by Astronomer is to follow the tutorial videos and watch the supplemental learning material. It's very useful.

I have no additional thoughts about Astro by Astronomer before we wrap up. I gave Astro by Astronomer a rating of eight out of ten.

My main use case for Astro by Astronomer is working with data pipelines, which involves extracting data from a database, transforming the data, and saving it to a data lake.

I deployed Astro by Astronomer on my local machine, which was very easy to deploy and run. We have Airflow in our infrastructure, but it is not provided by any cloud provider. Astro by Astronomer may be a great choice for us in the future.

I think the best feature that Astro by Astronomer offers is the self-hosted Airflow. Since we work by deploying Airflow in our infrastructure and managing the resources, it is very difficult to manage because we don't have a DevOps member with very advanced knowledge in Kubernetes. We try to run Airflow ourselves, but it has many problems because Airflow has many services that run along with the web server. A cloud solution would be great so we could focus only on the business rules of the data, not on the technical infrastructure.

A cloud solution like Astro by Astronomer could change my team's workflow and resource allocation as we would spend less time fixing infrastructure bugs and researching infrastructure challenges and could just focus on the pipelines and the DAGs of Airflow.

Even with my limited use of Astro by Astronomer, the positive impact on my organization is that I noticed a benefit when deploying on my local machine. I thought it was much easier than the Airflow documentation using Docker and all the tools that need to be deployed.

I don't know of anything frustrating about Astro by Astronomer, but I find the documentation very easy to understand. However, I didn't find information about the pricing initially.

I would like to add that the pricing could be improved. I see there is a starting price per hour, but a calculator to estimate the monthly cost for an organization using Astro by Astronomer Cloud would be helpful.

I used Astro by Astronomer once to deploy Airflow on my local machine.

I saved a significant amount of time compared to my previous setup. I deployed Astro by Astronomer on my local machine in about five minutes. With the Airflow documentation, it took much more time since we had to deploy the image and load the Docker Compose to see what needed to be changed, and it was very painful compared to the Astro by Astronomer deployment.

My advice to others looking into using Astro by Astronomer is that it's a great choice since we have a lot of experience working with Airflow and managing Airflow infrastructure is tough if you don't have a specialized team working on deploying Airflow in an on-premises service. I would recommend Astro by Astronomer to others that use Airflow in their day-to-day work.

I would rate Astro by Astronomer a nine out of ten. I chose nine out of ten because of the need for more transparency regarding the costs of this solution for the organization. More specifically, I would like to understand what Astro by Astronomer's advantage is compared to on-premises Airflow.

I can't really talk about Astro by Astronomer's AI capabilities, particularly its governance and security, because we don't have full experience with Astro by Astronomer in running our Airflow.

Regarding Astro by Astronomer's AI capabilities, I also don't have enough experience to comment on the accuracy and reliability of output.

Astro by Astronomer was deployed in my organization only on our local machines.

I installed Astro by Astronomer manually on our local machines and did not purchase it through any cloud marketplace.

I don't have any additional thoughts about Astro by Astronomer.

My main use case for Astro by Astronomer is integrating requests about our infrastructure and connections with the vCenter server. I use Astronomer to make these requests.

A specific example of how I use Astro by Astronomer with my vCenter server is that my vCenter has 5,000 virtual machines, and I need to get the specific VLAN every day for these virtual machines. I make a DAG to collect all the information about the VMs, VLANs, IPs, and virtual IPs of these virtual machines.

I also use Astro by Astronomer to update the information about space every day, including information and space about our storage systems, such as what is consuming space, what is free, and what is consumed. I use Astronomer to orchestrate all the collection, data, and treatment of this data.

The best features that Astro by Astronomer offers include easy deployment, which I believe is the important part because you can deploy your environments easily with Astro.

The easy deployment helps my team and my workflow because we are not specialists in this kind of environment deployment. This easier way to deploy helps us to skip this part and focus on what really matters to our team.

Astro by Astronomer has impacted my organization positively because all the automation that was done manually before is now automated and gives us more time to analyze and concentrate on important business matters.

I do not think Astro by Astronomer can be improved at this moment, as Astro is perfect to me, and I do not see any improvements that can be done.

If I had to think of one area where I see potential for improvement, it would be integration with infrastructure virtualized services like vCenter, Hyper-V, and storage systems like Hitachi and IBM FlashSystem. I think we need to use the native CLI of this equipment and create a kind of integration since Astronomer does not have this integration added to their flow.

I have been using Astro by Astronomer for one year.

In my experience, the scalability of Astro by Astronomer is wonderful and very precise.

I did not use any other solution previously.

Before choosing Astro by Astronomer, I did not evaluate other options because all of them were in the cloud and the idea was to deploy it into an on-premises system.

The advice I would give to others looking into using Astro by Astronomer is to have patience and read all the documentation. I rate this product a 9 out of 10.

My main use case for Astro by Astronomer is the creation of DAGs and orchestration with Airflow. I design data pipelines using Astro by Astronomer, mostly using Python and writing the necessary DAG files so that they can be interpreted as DAGs and executed through Airflow in an Astro by Astronomer environment.

The best features that Astro by Astronomer offers are fairly fast deployment, convenient integration with Airflow, and it is quite easy to scale.I was working on a project where, by having everything with Astro by Astronomer, it was easier to start Airflow and launch the DAGs, and these features made a significant difference for my team.Astro by Astronomer has had a positive impact on my organization as I believe deployment times have been accelerated. While I do not have the exact measurement of the times, I can confirm that deployment times have been reduced.

Astro by Astronomer could improve by reducing costs or decreasing infrastructure management and having less dependency on Kubernetes so that the infrastructure is simpler.

I have been working in my current field for about six or seven years.

I consider the platform to be stable.

The scalability of Astro by Astronomer is suitable for larger-scale projects and one of the advantages is that scalability is quite easy.

I did not evaluate other options before choosing Astro by Astronomer, as the decision did not depend on me.

My advice to other people who are considering using Astro by Astronomer is that they carry out some kind of use case or practice to see if it is useful for them. I would rate this product a seven out of ten.

My main use case for Astro by Astronomer involves monitoring a significant number of enabled DAGs, and I must check whether all pipelines are running successfully. As I belong to the operations department, I am responsible for troubleshooting any failures and determining the reasons for them so that I can restore them to a healthy state.

I can provide a specific example of a pipeline I monitor using Astro by Astronomer. We have many tables in Snowflake, and several of those tables have replication DAGs. The DAG performs historical sync, fetches all data, and places it into Snowflake. One time, the DAG was failing due to resource unavailability because we had clusters running with a maximum cluster setting of four, but due to a data spike, that was insufficient to process the huge amount of data. We discovered it was a resource error, so we changed the cluster from four to six. When it failed again at six, we increased the cluster to eight, which then worked successfully.

Monitoring and ensuring DAGs run while connecting to different servers comprises my main use case with Astro by Astronomer. When you open Astro by Astronomer, there is a variable column where you can add connections, accounts, and warehouse parameters, and that is also part of my day-to-day activity.

The best features Astro by Astronomer offers include a user interface that stands out most to me because it is very helpful. I was using MWAA for AWS as well, but I would say that the UI of Astro by Astronomer is far ahead of AWS MWAA.

What makes the user interface stand out for me is that it is easier to navigate. For instance, if you log into MWAA and compare it to Astro by Astronomer, you can see that if you hover over the DAG bar, it provides enough information that you do not have to go inside and check, such as the run duration, the time it took, or the number of runs. All that information is present just by hovering over a particular task. Another advantage is checking the logs; when you click on the log, it has the option to wrap or unwrap it, and copying the log and pasting it into your notepad is easy to perform. Importantly, the cron expression is written above the DAG monitoring bar, so it helps me check the schedule of the DAG.

Astro by Astronomer has positively impacted my organization by making work easier; tasks are getting completed in much less time than before. Even if we hire someone new, we provide training of one to two weeks, and they can easily adapt and work on it.

I can share specific outcomes: we were a team of ten people, but after migrating to Astro by Astronomer, we received additional projects because we had plenty of time. Now, fifty percent of the team works on Astro by Astronomer, and fifty percent work on other tools, which is a positive aspect we gained from Astro by Astronomer.

One challenge I find with Astro by Astronomer is the cost because it is relatively higher than other tools. If you could decrease the cost, it would be much easier for small organizations to use.

Regarding needed improvements, while my overall feedback is good, I believe new users may need a basic understanding of Apache Airflow concepts before they can use Astro by Astronomer effectively. Some advanced Airflow configurations are also intentionally abstracted, which is beneficial for simplicity, but it may limit users who require deep infrastructure level customization.

I have been using Astro by Astronomer for more than four years.

In my experience, Astro by Astronomer is stable.

Astro by Astronomer's scalability is impressive; it can easily handle growing workloads. Our data volume is high, and it is handling it smoothly.

The customer support for Astro by Astronomer is very good, fast, and reliable; however, I have never faced any situation where I had to contact customer support because it is very user-friendly.

My advice for others looking into using Astro by Astronomer is that it is very user-friendly, and you should choose it instead of any other tools. This is not one of those tools where you have to hardcode in the backend; it is very simple, and there are several courses available on the internet that you can watch and start using from day one. My overall rating for Astro by Astronomer is nine out of ten.

My main use case for Astro by Astronomer is when I need to orchestrate data to take the raw data, transform the raw data, and put it somewhere in databases. I need an orchestration product, which is why I'm using Astronomer.

A quick specific example of a project where I used Astro by Astronomer in this way is that I have orchestration for the raw data that is based in S3 as JSON files. I use Astronomer as the Airflow orchestrator. Airflow took all the data from the S3 buckets, started transforming by Spark (mostly Spark, sometimes DBT as well), and loaded all transformed data into the databases.

I also use Astronomer to orchestrate some Python scripts in addition to making Python projects that I can start independently.

The best features Astro by Astronomer offers include being easy to use and easy to install with one-button installation, as well as the possibility to work with Windows. The ability to use operators is also greatly appreciated, as you do not need to set some of the features inside Astronomer; you can just open it and everything works.

The possibility to work on Windows 11 has made the biggest difference for me in my daily work. When I use regular Airflow for other projects, I work in Linux, which makes Astro by Astronomer a significant improvement.

Astro by Astronomer has positively impacted my organization because it has helped me save time due to the big support and large community, allowing them to help me solve problems that I encounter while working with Astronomer.

If I had to pick one thing that could be improved, it would be the speed when working in clouds. Everything is great for now, including the possibility to work with cloud data such as Snowflake or DataBricks, so you can use Astro by Astronomer as an orchestration tool when working with this cloud infrastructure.

The needed improvements regarding speed is the main thing that is on my mind.

I have been using Astro by Astronomer for probably six months for some of my hands-on projects that I am making offline.

Astro by Astronomer's scalability is good; it handles growth and larger workloads well for me.

I previously used regular Airflow before I switched to Astro by Astronomer, as it is really slow and a bit harder to work with, which is why I am using Astro by Astronomer as my primary orchestrator.

I only evaluated regular Airflow before choosing Astro by Astronomer.

The community support has helped me because I encountered a couple of problems with some Python operators and I tried to search for them. I found that there is an Astronomer community where many people can help each other. At least 90 percent of all questions were answered in that community place.

Regarding Astro by Astronomer's governance and security features, I think they are great, but I did not use them quite often.

My advice for others looking into using Astro by Astronomer is that I had experience in Airflow, which is why Astro by Astronomer was much easier for me to use because I knew what to do and how to use it inside, as it is pretty much similar to regular Airflow. For other people, it may be better to use some basic, regular things first. However, if you have no time and you need to use it out of the box, it should work for you. Astro by Astronomer is my choice. I rate this product a 10 out of 10.

My main use case for Astro by Astronomer is scheduling workflows and jobs for my data engineering workloads.

An example of a workflow or job I schedule with Astro by Astronomer is EMR workloads which run on a Spark cluster; we schedule and trigger those using Astro by Astronomer. Additionally, we have some job roles which do data ingestion by reading files from SFTP and loading them to Snowflake, so we have those jobs scheduled on Astro by Astronomer as well.

I have used Astro by Astronomer for every cloud work scheduling as well as ad hoc Python jobs.

The best features Astro by Astronomer offers include a pretty good UI, and rerunning jobs is quite easy while clearing jobs is also straightforward since I don't have to go onto the cloud console to search for the job; the UI lists the jobs clearly and the schedule is very readable, so everything about it is excellent.

What I appreciate about the UI of Astro by Astronomer is that we sometimes need to rerun jobs and check for failures; it is easy to spot failed jobs using the red dot and then clear them. Reading logs is also very easy, especially compared to how we used to do it in AWS cloud previously where we had to go into each job and search for the log, so the UI is very good.

Astro by Astronomer has positively impacted my organization; with implementation and all jobs in one place, it has saved us a lot of time to monitor, rerun, and debug jobs, which helps in terms of the overall output that the team can deliver.

In terms of specific metrics or examples of how much time my team has saved, previously, our team used to get an alert and then go into the AWS console, log in, search for the job, and then search for the exact logs, but now we just go to the UI where we can see the list of jobs and check logs very quickly instead of going to the console, logging in, and managing accounts in AWS, so it has saved us considerable time.

Regarding how Astro by Astronomer can be improved, I don't feel there are many improvements needed right now, but I believe if I could have a bulk restart for a job or a specific start date and end date for a job rerun, that would be valuable for us.

I have been using Astro by Astronomer for probably around two years.

I don't think I want to add anything else about the features; for now, everything about it is something I appreciate.

The reason I give it a perfect score is that compared to other products, the readability and ease of use make Astro by Astronomer the best product.

Regarding Astro by Astronomer's AI capabilities, I haven't used the AI side of Astronomer yet; for now, I have used it for standard use cases, but I will probably explore that in the future.

I think the accuracy and reliability of output from Astro by Astronomer are quite reliable; we do need a human in front of it to verify, but still, I would say it is 60 to 70 percent reliable.

My advice to others looking into using Astro by Astronomer is that they should definitely try it once, and I am certain they would find it valuable if they have certain workloads where they want them in one place. I give this product a rating of 10.

The main use case for Astro by Astronomer is a data engineering pipeline where I can create ETL jobs. Astro by Astronomer is a platform where I can easily create data pipeline jobs and perform ETL extract operations.

The best features Astro by Astronomer offers is the easily created pipeline without doing much code. Astro by Astronomer makes it easy for me to create pipelines because the interface is intuitive; I can work with no code basically, and I can click on blocks and set up my pipeline the way I want, configuring it in a way that is suited to my needs.

Astro by Astronomer has positively impacted my organization because I can create more data pipelines to integrate into my medallion architecture, making it easy to do data engineering jobs. This has made my workflow faster.

Astro by Astronomer has helped me complete my tasks more quickly because I spent less time coding the pipeline since there are not many things to code using Astro by Astronomer.

Astro by Astronomer's governance and security capabilities are very good as it is a very secure platform. The accuracy and reliability of the output from Astro by Astronomer's AI capabilities are as great as many other platforms out there.

I don't know anything that could be improved about Astro by Astronomer. If I had to think of one thing, it would be nothing in terms of a small tweak or feature. Everything about data handling and visualization is fine the way it is right now.

I have been using Astro by Astronomer for about one year, maybe one year and a half.

I would give advice to others looking into using Astro by Astronomer about reading the documentation; it is very simple and easy to use, so you will be fine. I gave this review a rating of 9.