Apache Airflow® with Astro by Astronomer - Annual Plans

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.

Astro by Astronomer allows me to view a complete pipeline, check the logs, and rerun jobs. I have also been using it for environment management and automatic scaling. I have been building data pipelines with Astro by Astronomer so that I can check tasks, see which task has failed and why it failed, review logs, and examine errors.

I can retry tasks multiple times if there is a failure. In one project, we used Astro by Astronomer to solve a problem where we had a pipeline ingesting 50 to 60 GB of data from cloud storage into our bronze, silver, and gold layers using Databricks. We use Astro by Astronomer for orchestrating the entire workflow. Each step—data ingestion, validation, transformation, and loading—was a separate Airflow task with dependencies. If one task failed, Astro by Astronomer would automatically retry it, send an alert, and allow us to rerun only the failed task instead of running the complete pipeline. This reduced our manual intervention, improved reliability, and made jobs easier to monitor and troubleshoot.

Astro by Astronomer is more reliable. In addition to orchestrating these pipelines, we use Astro by Astronomer for CI/CD development. We package our DAGs and deploy them consistently across development, QA, and production. We use the Astro UI to monitor pipeline health and troubleshoot failures. We also integrated it well with Databricks, cloud storage, and databases, which makes it easier to build reliable and scalable pipelines and to monitor these jobs. Overall, it has reduced our operational overhead and improved the reliability of our data pipelines.

Astro by Astronomer standard Airflow deployment time was reduced by more than 40 to 50 minutes overall, where manual effort was five to ten minutes per automated deployment. We also saw production pipeline failures decrease by roughly 20 to 30 percent because of better dependency management and consistent deployments. When failures did occur, troubleshooting time was reduced by around 40 to 50 percent since the logs and monitoring made it much easier to identify the root cause. Overall, the team saved several hours each week that were previously spent on manual deployments and operational support. We have observed noticeably fewer deployment issues, faster recovery from failures, and a significant reduction in manual effort due to automated deployments and better monitoring.

One of the best features Astro by Astronomer offers is Airflow deployment, built-in monitoring, and seamless CI/CD integration. I also appreciate the ability to manage different environments like development, QA, and production consistently. The task retry mechanism, alerting, and detailed logs make troubleshooting much easier for me. Since we integrate Astro by Astronomer with Databricks and cloud storage, it also helps us orchestrate complex data pipelines reliably without spending much time managing Airflow infrastructure.

CI/CD has made our workflow much more efficient by automating deployments with Astro by Astronomer. Whenever we make changes to our DAG, the code is pushed to a Git repository, and the CI/CD pipeline automatically runs validation and tests before deploying to the Astro by Astronomer environment. This reduces our manual deployment errors and ensures consistency in releases across development, QA, and production, which allows the team to deliver changes faster. It also lets us roll back to the previous version if an issue is found. With the alert mechanism, if there is any failure in a job, all users who have been associated with that particular job get notified immediately. As for the retry mechanism, if I have a pipeline with more than 50 tasks and a particular task is failing, we can run just that particular task instead of running the complete pipeline.

One of the biggest improvements since we started using Astro by Astronomer is that it has reduced our manual workflow. Since we have adopted Astro by Astronomer, we have seen pipeline deployments become much faster and easier in all specific environments. We do not need to change the code for a particular environment; Astro by Astronomer picks the configuration based on the environment. If it is deployed in development, it picks the configuration from development and runs the task. If it is deployed in QA, it picks the configuration for QA and runs the task, and the same applies for production. This reduces our manual workload, and continuous deployment is much easier. Pipeline failures also decreased due to built-in retries and better monitoring. When issues did happen, we could identify and resolve them much more quickly using the detailed logs. In the detailed logs, we can find at which stage the code failed, what the issue was, and what time it failed. It has reduced our manual effort and improved the reliability of ETL workflows. It has also improved downtime, so downtime has been minimized, which helps us ensure data is delivered on time for downstream reporting and analytics. From a business perspective, we have achieved faster deployments, reduced manual effort, improved reliability, and faster troubleshooting.

Overall, my experience with Astro by Astronomer was very positive, but there are a few areas where it could improve. For large environments with hundreds of DAGs, navigation and monitoring could be more intuitive, making it easier to identify critical issues quickly. More built-in analytics for pipeline performance and resource utilization would be valuable.

Additionally, while debugging is already good, having more detailed error insights and root cause analysis out of the box would help reduce troubleshooting time. For example, if an error is happening repeatedly in a particular log, could Astro by Astronomer itself identify the error in the log and rerun the job based on that particular error? These are areas where it can improve.

I have been using Astro by Astronomer for the last four years.

The documentation for Astro by Astronomer is fine. I have always preferred the documentation. You can get detailed information about tasks, how to write them, the syntax, and all features. I have also gone through the certifications of Astro by Astronomer, so that also helped me analyze what features are coming into the latest release, and that has been helpful for me.

Regarding governance and security, I think Astro by Astronomer is sufficient. No additional improvement is needed. I have not faced any challenges with governance and security. I was not very involved in it, so I cannot identify many issues.

The AI capabilities of Astro by Astronomer are generally reliable for routine tasks, such as generating DAG templates, providing code suggestions, and assisting with troubleshooting. For a production pipeline, I always review and validate the logic before deploying. Occasionally, the suggestions need more refinement for complex workflows or organization-specific requirements, but overall, I have found the output to be accurate and useful.

My advice to others looking into using Astro by Astronomer is to start with a well-designed Airflow architecture and follow the best practices for DAG design. Use Git and CI/CD from the beginning. Keep tasks modular and implement proper monitoring and alerting. Astro by Astronomer takes away much of the operational overhead of managing Airflow, so teams can focus on building reliable data pipelines instead of maintaining the infrastructure. For organizations running multiple ETL workflows, especially on Azure and with Databricks, Astro by Astronomer is a strong choice because it improves deployment consistency, scalability, and operational efficiency.

Astro by Astronomer is beneficial. I have used it in my project, and it is very beneficial for us. It has improved performance and reduced our workflow. I would always prefer Astro by Astronomer instead of any other technologies. I would rate my overall experience with this product as a 9 out of 10.

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 orchestrating the Airflow pipeline without hassling the backend of Airflow.

I use Astro by Astronomer as a managed platform for Apache Airflow to orchestrate our data engineering workflows. We primarily use it for scheduling, dependency management, monitoring, and retrying ETL/ELT pipelines rather than executing heavy data processing itself.

I use Astro by Astronomer day-to-day for ingesting data from APIs or cloud storage, running Azure Data Factory pipelines where required, triggering data based on PySpark transformations, performing data quality checks and validations, storing created data into a data warehouse or data tables, and sending notifications on success or failure through emails or notifications.

Astro by Astronomer has positively impacted our organization by helping us use task groups to organize complex DAGs. We configured tools to control concurrency and avoid overloading downstream systems. We use dynamic task mapping for processing multiple files or datasets. The UI makes it easy to visualize dependencies or rerun only failed tasks instead of the entire pipeline. The log of each task is available directly from the UI, which speeds up debugging.

Using Astro by Astronomer is definitely improving our team's productivity. Before, tracking failed jobs and their dependencies was more manual. With Astro by Astronomer's monitoring dashboard and centralized logs, we can identify failures much faster and rerun only the failed tasks instead of restarting the entire workflow.

The best features that Astro by Astronomer offers include reduced operational overhead for managing Airflow. It provides managed Airflow with easier upgrades, CI/CD integrations, and simplified development environment management, allowing the teams to focus on building pipelines instead of maintaining Astro by Astronomer infrastructure.

It is very easy to use Astro by Astronomer. One of the biggest advantages is its ease of use. It provides a clean UI to monitor DAGs, task dependencies, check logs, and manually trigger or rerun failed workflows. Troubleshooting is much easier because we can quickly identify which pipelines failed and where.

The current version of Astro by Astronomer is good enough for our needs.

The documentation part of Astro by Astronomer can be made easier and more generic, not specific to the integration with each of the services. There are a few areas where improvement would be helpful. While it integrates well with Airflow and cloud services, configuring some third-party agents can require additional setup and troubleshooting.

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

Overall, Astro by Astronomer is a stable platform. In my experience, we did not face any major outages caused by Astro by Astronomer itself. Most issues we have encountered were related to downstream systems, such as database cluster availability, API downtime, database connectivity, or temporary network issues, rather than the orchestration platform.

Regarding Astro by Astronomer's scalability, I would say it is one of the strengths of Astro by Astronomer. As the number of data pipelines and scheduled jobs increases, Astro by Astronomer handles the additional workload well. Since it is a managed Airflow platform, it can scale its underlying worker nodes to support more concurrent DAGs and tasks without requiring us to manually manage infrastructure.

Regarding Astro by Astronomer's governance and security capabilities, I have positive thoughts. From a governance and security perspective, Astro by Astronomer provides several capabilities that help us operate securely. We do not store secrets such as database passwords or API keys directly in our DAGs. Instead, we use Airflow connections variables or have integrated with a secret manager like Azure Key Vault to securely manage credentials.

Before adopting Astro by Astronomer, we primarily relied on a combination of Azure Data Factory for orchestration purposes. We had some self-managed Airflow flows. As our data platform grew, managing complex dependencies, monitoring pipelines, and scaling our system became more challenging.

Before choosing Astro by Astronomer, we evaluated Azure Data Factory as the external tool for data movement and simple orchestration. For complex workflows with many dependencies, custom Python logic, dynamic task generation, and integration across multiple systems, Airflow and therefore Astro by Astronomer offers much greater flexibility and control.

Astro by Astronomer is very consistent and reliable 99% of the time, although sometimes it lags. Overall, the reliability has been very good. Once a DAG is configured and tested, the output is consistent because Astro by Astronomer ensures tasks are executed in the correct order and respects dependencies.

I would recommend Astro by Astronomer to organizations that already use or are planning to use Apache Airflow for workflow orchestration, especially if they want a managed solution instead of maintaining Airflow themselves.

I gave this product a rating of 8 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 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.