Astro by Astronomer Reviews

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.

I have been using Astro by Astronomer CLI as a local development environment for about a year. During this period, it has been the main tool I use to develop, test, and validate my Apache Airflow projects before deploying them to production. In production, I use Apache Airflow together with Astronomer Cosmos to orchestrate dbt pipelines.

My main use case for Astro by Astronomer is using the CLI as a local development environment for Apache Airflow projects. I use it to develop, test, and validate DAGs before deploying them to production, ensuring everything works correctly. In addition, in my day-to-day work, I use Astronomer Cosmos to integrate and orchestrate dbt pipelines with Apache Airflow, running data transformations on Amazon Athena with Apache Iceberg tables.

In my day-to-day work, I use Astro by Astronomer CLI to develop and test new Apache Airflow DAGs locally before publishing them to the production environment. For example, when I need to create a new data pipeline with dbt using Astronomer Cosmos, I first validate all the orchestration locally with Astro by Astronomer CLI, check that the dependencies, tasks, and integrations are working correctly, and only then do I deploy it to the production environment. This reduces errors and makes the development process much faster and more reliable.

In addition to using Astro by Astronomer, I also use Astronomer Cosmos to integrate dbt with Apache Airflow. This makes creating and maintaining DAGs much easier because Cosmos automatically generates the dbt tasks, respecting the dependencies between models, making orchestration simpler and more organized. In our environment, dbt runs happen in containers on Amazon ECS, using AWS Fargate. This model allows us to start resources only during pipeline execution and shut them down afterward, avoiding having dedicated servers running all the time. As a result, we were able to reduce infrastructure costs, maintain a scalable environment, and execute transformations efficiently, especially for workloads that do not need to be active continuously.

The main value of Astronomer Cosmos is simplifying the development and operation of pipelines with Apache Airflow. Astro by Astronomer CLI offers a very consistent local development experience, while Astronomer Cosmos makes it easier to integrate dbt with Airflow by automatically generating the DAGs and respecting the dependencies between models. In addition, this approach integrates very well with Amazon ECS and AWS Fargate to run the dbt jobs. This allows us to scale on demand and pay only for the resources used during pipeline execution, reducing infrastructure costs without sacrificing reliability and ease of maintenance.

A differentiator I consider very important is that Astronomer Cosmos is very flexible and integrates easily with other modern data engineering technologies. In my case, the combination of Astro by Astronomer CLI for local development, Astronomer Cosmos to orchestrate dbt projects, and Amazon ECS with AWS Fargate to run the jobs has brought a very efficient workflow. Besides facilitating the development and maintenance of pipelines, this architecture allowed us to reduce infrastructure costs, because the dbt containers are started only when needed and shut down at the end of execution. This offers a good combination of productivity, scalability, and operational efficiency, especially in environments that run on-demand workloads.

The positive impact of Astro by Astronomer has mainly been on team productivity and the standardization of development. Astro by Astronomer CLI made it much simpler to create and validate Apache Airflow pipelines in a consistent local environment, reducing configuration issues and speeding up testing before deployment. In addition, with Astronomer Cosmos, we were able to integrate dbt with Airflow in a much more organized way, automating the creation of DAGs.

A practical example is that we were able to significantly reduce the time needed to develop and validate new pipelines because all developers work in the same local environment using Astro by Astronomer CLI. This reduced configuration issues and decreased rework during deployments. Another important result was the reduction in costs for running dbt. Instead of keeping dedicated infrastructure running all the time, we started running the jobs in containers on Amazon ECS using AWS Fargate, which are started only when needed and shut down at the end of execution. Although I cannot share exact numbers for confidentiality reasons, we observed a relevant infrastructure cost saving, as well as a more scalable and simpler environment to operate. We also noticed a reduction in failures related to the integration between Airflow and dbt, thanks to the use of Astronomer Cosmos, which automates the creation of DAGs and ensures that dependencies between models are respected.

I believe one area for improvement for Astro by Astronomer would be to further expand the documentation and examples for more advanced scenarios, especially involving integrations with AWS, Amazon ECS, AWS Fargate, and Astronomer Cosmos. Although the documentation is good, some more complex use cases require additional research or testing until you find the best approach. It would also be interesting to offer more templates and ready-made best practices for modern architectures with dbt, Airflow, and Kubernetes, making it easier for teams that are just getting started. This would reduce the learning curve and further speed up the implementation of production environments.

The main point would be to provide more content and reference architectures for large-scale corporate environments, especially involving Airflow, dbt, Kubernetes, and AWS. This would help teams adopt best practices more quickly and reduce the time spent on architecture decisions. Otherwise, I consider the experience very positive, and Astro by Astronomer platform meets the needs of development and orchestration of data pipelines very well.

I have been working in technology for about twelve years, and specifically as a data engineer for approximately five years. During this time, I have worked at different companies and on different projects, always focused on data platforms, architecture, data processing, and cloud solutions. I currently work on the evolution and support of a data platform using technologies like Apache Airflow, dbt, AWS, and Astronomer Cosmos.

The interview was good and well structured. The questions covered the main aspects of the tool, such as user experience, benefits, improvement points, and business impact. If I could suggest some improvements, I would avoid very similar questions. At times, there was repetition, such as asking if I wanted to add something right after practically every answer. I would give more room for technical examples. Since the audience is in technology, questions about architecture, integrations, implementation challenges, and best practices would generate richer reviews. I would try to reduce administrative questions at the end—name, company, reference, contact—putting them in a form instead of asking all of them by voice. I would allow slightly more natural answers, without interrupting the interviewee between one question and the next. Overall, I found the experience positive, objective, and easy to follow.

In the flow, Cosmos unites data and ideas, and simplicity grows. I would rate this experience a nine 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.

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.

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I have been using Astro by Astronomer for around three years.

I would rate Astro by Astronomer nine out of ten because it is good and I have enjoyed using it.

My main use case for Astro by Astronomer is mostly for bringing data and ETL, scheduling, or working with large-scale datasets.

An example of how I use Astro by Astronomer with my large-scale datasets is by using the operators and the guided workspace that provides me all the things that I need for getting the data structured and pre-processed before using it in the data warehouse tables.

The best features Astro by Astronomer offers include a large operator and library that I can work with for different use cases.

Astro by Astronomer has positively impacted my organization by having a managed tool that we can all share and see in real time. Since we are just starting to use it, it seems to work.

Astro by Astronomer can be improved by adding a full library for dates, working, and interfacing with the diagrams. That is the main thing.

I have been using Astro by Astronomer all the time for two years.

Astro by Astronomer is stable.

The scalability of Astro by Astronomer for my needs works great.

I did not previously use a different solution; Astro by Astronomer is the only solution I have used. It is very great, and I love it.

I don't know if I have seen a return on investment because only the company knows.

Before choosing Astro by Astronomer, I did not evaluate other options because it is the most popular one.

On a scale of one to ten, I would rate Astro by Astronomer an eight. The reason I rate it an eight is that I think it is perfect, but I shared what I think needs to be improved and I think it is good.

Regarding Astro by Astronomer's AI capabilities, I have not used them, so I do not know about their accuracy and reliability of output.

The documentation and learning curve for Astro by Astronomer are very good and very detailed.

The monitoring and alerting functionality for my workloads in Astro by Astronomer is very good, and I love it.

I handle user permissions and access control within Astro by Astronomer, and everything is great, easy, and good.

The version control and change management for workflows in Astro by Astronomer are very good, and I love it.

My advice for others looking into using Astro by Astronomer is that they will use it and it will be great. Their life will be awesome.

I use Astro by Astronomer, specifically Apache Airflow, to develop data pipelines in our company. I used the knowledge I gained from the Astronomer certification for that purpose.

I use Astro by Astronomer specifically with Apache Airflow, which is the same orchestration tool we use for all our data pipelines.

I find it valuable to use Astro by Astronomer as an orchestrator. All these aspects are helpful when I consider its value.

Astro by Astronomer has impacted my organization positively because it is based on Apache Airflow. A person familiar with Apache Airflow can use Astro by Astronomer more easily.

It is mainly an improved version of Apache Airflow, and it has improved everything from the open source Airflow.

I do not have an idea about Astro by Astronomer's AI capabilities in governance and security. However, I think Astro by Astronomer's AI capabilities are more reliable than Apache Airflow open source.

In my experience, Astro by Astronomer is stable.

I do not have anything in my mind on how Astro by Astronomer can be improved.

Astro by Astronomer could offer tutorials or courses regarding Apache Airflow, not only for Astronomer's certification.

I do not have much idea on additional needed improvements for Astro by Astronomer.

I have been using Astro by Astronomer for three to four years now.

In my experience, Astro by Astronomer is stable.

I do not have much idea on Astro by Astronomer's scalability because I used it in an on-premises environment for my learning purposes.

I have not interacted with the customer support for Astro by Astronomer.

I have not used any different solution before Astro by Astronomer.

Since I mainly used the certification, it improved my knowledge from that, and it helped me to improve my time in my work.

It helped me work faster from what I can see.

I think Astro by Astronomer has a moderate price regarding pricing, setup cost, and licensing.

Before choosing Astro by Astronomer, I evaluated Apache Airflow as an option.

I do not have much detail to add about my main use case for Astro by Astronomer.

When I started using Astro by Astronomer, they have been upgrading it.

It is better if you learn Astro by Astronomer as a data engineer, and try different environments and different orchestrators.

I give this review a rating of 8.