Astro by Astronomer Reviews

We manage the deployment, governance, and everything for the different teams that want to orchestrate their workflows. All of their ingestion jobs, their analytics jobs, and their whole ETL or ELT jobs are built by the teams themselves, and we manage all of the deployment and governance for those jobs.

One of the jobs we handle involves consuming data from different sources like Google Analytics and Apple Analytics. We use dltHub to have a standardization layer across the different ingestion jobs. We source the data from there, pass it through a schema check, and then sync it to our Databricks table. We also run quality checks on them and then publish the final reports.

In the data platform, we use two different workspaces. One is for the admin deployments, which we, the central data team, use to orchestrate jobs such as rotating the credentials on schedule or running freshness checks and data quality checks. We then have a general workspace which different teams deploy their DAGs into. We use two branches for that: staging and main. We do not want anyone to deploy broken DAGs, so we have CI gates. Astro by Astronomer workspace cloud is connected to our main Astro by Astronomer repo. We have dispatch workflow jobs in GitHub which get triggered whenever there is a deployment in the child repos where the stakeholders deploy their DAGs. The dispatch workflow syncs the changes from the child repo to the main Astro by Astronomer repo. Based upon the change, if it is a simple DAG change, then we do not do a full deploy but just a DAG deploy, which is much faster. If there is a change in the infrastructure, then we do a full image deploy of Astro by Astronomer to that workspace or to that deployment.

There are a lot of new features that we have started using in Astro by Astronomer. Some of them I can list is their new AI agent, Auto. It is quite useful for investigating why a DAG has failed and getting more context around it. The other feature is Cosmos, where since most of our jobs are running dbt models, Cosmos provides us a way to run dbt natively. We can have granular-level logs and retries and lineage and everything. That is a useful feature.

Then there are Datasets where we can link different DAGs and to have a comprehensive framework and have a freshness check and quality checks on the entire stack. The Blueprint feature is quite useful since many of our ingestion jobs that are being created by stakeholders may not be quite familiar with Airflow, and with Blueprint, we provide them a way to use a no-code solution to build their DAGs. Astro by Astronomer Observe has a lot of features that we use: the observability and alerting that we set up.

Apart from that, the whole having the entire deployments and the DAGs and everything in one place is quite useful. In MWAA, everything is decentralized, and that creates a lot of friction and problems. In Astro by Astronomer, I find it really useful that everything can be in one place. Also, one of the good features in Auto, we can do the migration from, let's say, Airflow 2.x to 3.x using Auto seamlessly.

Instead of giving generic messages in, for example, a Slack channel when an Airflow DAG fails, you can use Auto to have a comprehensive root cause analysis of what has failed and based upon the context of the DAG and its historical runs, it gives a much more comprehensive analysis of what has failed, and that speeds up the fixing the bug and fixing the DAG cycle a lot more. Instead of going through the entire debugging process, having the root cause analysis provided helps tremendously. Sometimes we have to dig in a bit deeper, but it does help a lot.

Previously when we were using MWAA, since our DAGs were living in S3 buckets, the workers running in EC2 and having logging in CloudWatch, everything was scattered around the place and doing a deployment or debugging was quite a pain. Astro by Astronomer solves that seamlessly, where in one place, we have everything. Granting access control is quite easy. We have created different teams in Astro by Astronomer and integrating those teams with Okta, we can have seamless integration of which user can access which workspace. Also, whenever a user leaves the organization, their Okta is disabled and automatically they lose access. In that regard, Astro by Astronomer has helped tremendously.

When we have to do an upgrade, for example, upgrading the Airflow version, that was quite problematic in MWAA, and there used to be a downtime of approximately 40 to 45 minutes while the Airflow web server had to spin up and do its thing. That seems quite seamless in Astro by Astronomer.

Also having alerts, whenever a DAG fails, we have a direct PagerDuty integration for the important DAGs. If that DAG fails, we immediately get notified on our PagerDuty schedule. That is also one of the features that helped us tremendously. Having monitoring on top of that, Astro by Astronomer, integrating it directly with DataDog has been quite useful. That was quite challenging when using MWAA. Additionally, Astro by Astronomer seems to be quite cheaper than MWAA overall.

I would say it would be much more helpful if Astro by Astronomer can provide an MCP to use and integrate with Astro by Astronomer and their AI agent Auto, so that we can use our tools like Claude, Code, or Cursor to have a central location where we integrate that MCP in Databricks via the AI gateway, and then we can have a place where we can connect to Astro by Astronomer, Databricks, other tools such as Jira and DataDog, and then have a much more comprehensive analysis and overview and monitoring of the entire data ecosystem.

Having an MCP integration would tremendously help us to have a more comprehensive data platform that is more AI-ready. That is one of the features that I can think of. Another thought would be having an integration with Kubernetes instead of just scheduling everything on Astro by Astronomer workers. That would be helpful.

One thing I can think of is that the Astro by Astronomer local developer environment can be improved further, but perhaps that is a very niche use case that I have in mind, and it may not be useful for others.

There is room for improvement around having an improved UI and also integration with other tools.

On governance, I would say we can have a more granular level of governance even inside workspaces. Having much more granular control over people belonging to the same team in Astro by Astronomer could have different access to different DAGs, depending upon our tagging strategy. That is one of the areas I can suggest in terms of improvement.

The AI capabilities can be improved further. I would compare the current AI capabilities to newer models provided by Claude such as Opus or Codex.

I have been working as a Data Engineer for more than eight years. I started at Babbel last year, so I am almost going to complete one year at Babbel.

It was not 100% seamless, since there were a few complexities that we had to overcome. Some of our DAGs were tightly coupled with how MWAA works, particularly the whole IAM roles and everything. Additionally, Secrets Manager was an AWS native product. In MWAA, there was a seamless integration with that, but when we migrated to Astro by Astronomer, those were some of the challenges that we had to overcome. It was quite manageable. I would give that experience an 8 out of 10.

Scalability-wise, it is very good. We do not have to worry about selecting the right worker size, and it is quite scalable. We did hit the limits sometimes, but that was due to us not estimating the workload properly. However, with the monitors, the DataDog monitors and the Astro by Astronomer alerts that we had in place, we were notified quite in advance, and we could increase the worker capacity in time. That was good. We also use the Astro by Astronomer executor, which combines the features of both Celery and Kubernetes executor, so the overall experience was quite seamless.

Customer support is great. We have support engineers helping us if we face any issues. Additionally, we have a bi-weekly call with the Astro by Astronomer product team where they guide us to the new features and help us if we can use some of their new features or if we are following the best practices in our current setup. It is quite good.

We were using MWAA for Airflow before that. We switched because of the reasons I have already mentioned.

Astro by Astronomer acts as our orchestration layer. All of our jobs, including our ingestion jobs, our transformation jobs, analytics, machine learning, and AI workloads, are everything orchestrated using Astro by Astronomer now.

We had a call with the Astro by Astronomer product team, and then they pitched us the idea, and then we got onboarded with them.

Our MWAA workers and everything, including deployments and everything, used to cost us around three thousand dollars previously to run the entire stack. Now, currently, after Astro by Astronomer, we could manage it for probably one thousand five hundred to one thousand eight hundred dollars per month. That is quite a significant improvement.

Whenever there is a deployment, Astro by Astronomer seems to have zero downtime, which used to be around 40 minutes when doing an MWAA deployment.

If you want a one-stop solution for your orchestration, I think Astro by Astronomer is the best place to look. The overall product experience is quite good. The customer support is good. You should definitely check out Astro by Astronomer. I would rate this solution an 8 out of 10.

My main use case for Astro by Astronomer is managing Airflow as an Astronomer. I am building ETL workflows that get scheduled by Airflow DAGs. I use Astronomer as an instance or as a service of Airflow.

A specific example of how I am using Astro by Astronomer in my ETL workflow is that I have almost 500 to 600 different data pipelines where data gets ingested from different sources to the warehouse. The sources are hybrid by nature, and the target is also hybrid, but most of the time I am using Snowflake and Databricks to ingest data. Between the sources and targets, there are many steps to get data ingested, with different dependencies between the tasks and between the jobs. I have completed several API integrations using Airflow DAGs, which get executed based on events.

The ETL jobs and data pipelines are my main use cases. I also have some online, on-demand jobs that get executed by event-driven workflows. These are the two main workflows that I am working on currently.

Previously, I used an AWS-managed service as Airflow. After moving to Astro by Astronomer, my team spends considerably less time on Airflow infrastructure maintenance and more time delivering new data pipelines. Deployment has become more standardized, and production stability has improved. Troubleshooting has become faster due to the centralized monitoring system. My team spends less time on Airflow infrastructure maintenance while deliverables, quality, and the development lifecycle have improved significantly.

The best features Astro by Astronomer offers include that it is a managed Airflow platform, so it eliminates the complexity of maintaining the Kubernetes cluster or the scheduler metadata database. I also appreciate the Astro CLI, which makes local development and testing very easy. Developers can validate the DAGs locally. I value the scalability because my tasks have more than 500 jobs running, and automatic scaling handles increased workloads without requiring manual infrastructure changes. I appreciate the CI/CD integrations because it works well with the Git-based deployment workflow, and that helps me manage release and version management. The observability is also excellent with centralized logs and execution histories, which helps with troubleshooting.

Regarding how centralized logging and observability have helped my team with troubleshooting, my data pipeline is very complex with many interdependent elements. Often, an intermediate task fails, and dependent tasks need data to flow at a specific time. If the data has not arrived, I need to investigate and find the root cause. Finding the root cause is very difficult when many complex, deeply nested tasks are interdependent. The logger and observability help me find these issues very easily.

Astro by Astronomer has impacted my organization positively because the business impact is quite good. Previously, I used an AWS-managed service as Airflow, but after moving to Astro by Astronomer, my team spends considerably less time on Airflow infrastructure maintenance and more time delivering new data pipelines. Deployment has become more standardized. Production stability has improved significantly, as I found many issues when using AWS-managed services. Troubleshooting has become faster due to the centralized monitoring system.

Since switching to Astro by Astronomer, I can share specific outcomes and metrics, such as time saved. I had many difficulties when using Airflow through other service providers. When more tasks suddenly needed to work in parallel, I experienced resource constraints. This triggered many failures of downstream jobs. Development was more time-consuming. The CLI helps me perform local development much faster. Previously, finding the root cause of any issues took almost half a day. This observability is helping me find the exact problem within an hour, sometimes in minutes. The development lifecycle is shorter and much less error-prone. Previously, developing DAGs took days and sometimes a sprint. Now, I can deliver within a couple of hours.

I believe there are several areas where Astro by Astronomer can improve. The pricing has become a little expensive because I have very large and always-running workloads. The enterprise configuration sometimes requires a learning curve that could be easier. Advanced customizations could be improved, and the documentation of complex topics needs enhancement. In enterprise scenarios, better documentation would help me more.

I believe support is strong. Whenever I reach out to find issues or root causes, I get a very timely response from the support team. Integration is definitely an area that could be much better because of advanced enterprise requirements that need more configurable learning curves. If integration were easier, it would be simpler to deploy to different teams.

I have been using Astro by Astronomer for almost two years.

At this moment, I have seen a couple of hiccups with Astro by Astronomer, but most of the time, I can say it is very stable.

The scalability of Astro by Astronomer is good and quite easy, and it is maintained as a service by the Astronomer team. At the beginning, I saw some hiccups. I was not getting the number of instances I was looking for, and jobs got stuck, taking more time. However, day by day, it has become more stable.

Customer support for Astro by Astronomer is good. I get a very quick response. I have some SLA agreements, and more than 95 percent of the time, I have seen those SLAs get met.

I used to use MWAA of AWS. Different teams still work on that, but I found difficulties with that in terms of maintainability. I believe that is one of the reasons I switched many DAGs to Astronomer.

I believe I have started seeing ROI now since I have been using Astro by Astronomer for more than one year. It is hard for me to say that money has been saved, but definitely time has been saved. I am still working with the same number of employees, but the deliverables, quality, and development lifecycle have become shorter.

I believe my experience with pricing, setup, cost, and licensing could be better. It is a little expensive for my organization when running many large and always-runnable workloads. The pricing is on the higher side. In terms of licensing, the cost could also be better. I think it would be beneficial if pricing were more flexible based on organizational workloads and offered dynamic pricing options.

Before choosing Astro by Astronomer, I tried to build my own Airflow instance to run in my own data center. I found that in terms of the complexity of the job and the task, it is difficult to maintain ourselves and would need a very dedicated resource. That is why I stopped that approach and moved to Astro by Astronomer.

I believe there are definitely many commendable features that work very well, but there are many areas of improvement as mentioned previously. I believe day by day, as those improvements come and are available, this platform will continue to get better and better.

Regarding Astro by Astronomer's AI capabilities, I think governance and security are important because I am not using much of the AI capability at this moment due to security and governance concerns. I am very careful about how to use that specific service, and I am trying to learn more. I have many open questions for the Astronomer team to understand that specific area.

I believe the accuracy of Astro by Astronomer's AI capabilities is pretty good. In terms of accuracy, depending on the required prompt, a better prompt gives a better result. That helps me get closer to the development target. As accuracy is definitely not 100 percent, I can see developers saying they are getting 70 to 75 percent accuracy, and that is definitely helping with the reliability of the output.

My advice to others looking into using Astro by Astronomer is that I would definitely recommend it. I would recommend Astro by Astronomer to organizations that rely heavily on Apache Airflow workflows and want a production-ready managed platform. It is very valuable for medium to large enterprises or data engineering teams that prioritize delivery.

Astro by Astronomer is doing a great job. I believe my relationships with this platform will continuously grow as my services grow. I am looking forward to integrating and leveraging the AI service and capability with Astronomer. I would rate this platform a 7 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 is to build pipelines, specifically data pipelines, similar to an Apache Airflow tool which I use for day-to-day data engineering tasks.

A specific example of a pipeline I've built with Astro by Astronomer is related to a project involving YouTube, where I obtained the data from the source and had to build a pipeline to send it to a destination called an S3 bucket so that downstream analytics can be performed on top of that, such as using it for AWS QuickSight and making dashboards.

The best features Astro by Astronomer offers are CI/CD integration, which allows us to integrate seamlessly with version control tools like Git. It has managed Airflow built-in, which automatically handles infrastructure scaling, schedulers, and workers. This reduces operational overload.

The CI/CD integration in Astro by Astronomer has helped me because previously, deployments were done manually, which used to take a lot of time. Now with continuous integration and continuous deployment, along with the scripts we write, the performance is really good and deployments take very little time. 

Astro by Astronomer can be improved by adding managed infrastructure with zero setup deliverable developer tools. It can include a high-performance executor, which eliminates manual Airflow maintenance through cloud-hosted or remote execution deployment models. Key improvements for Astro by Astronomer include integrating with Terraform provider and multiple other tools.

Regarding Astro by Astronomer's AI capabilities, I think its governance and security need to improve, but currently it can be used quite well, though there are some improvements needed for governance and security related to AI.

I have been using Astro by Astronomer for the last one year to build my personal projects.

Astro by Astronomer is stable.

The scalability of Astro by Astronomer is really good and it is very scalable.

The customer support of Astro by Astronomer is really good.

I have seen a return on investment with Astro by Astronomer as it has helped me reduce a lot of time when building pipelines. Before we used to use Apache Airflow, now it is integrated inside, so it has saved a lot of time. The investment which we have made is very good in terms of return.

My experience with pricing, setup cost, and licensing for Astro by Astronomer is that I am using a personal account, so I think it is good, but the prices could be reduced.

Regarding Astro by Astronomer's AI capabilities, I have not used its AI features, but I can use it and make sure it works fine.

My advice for others looking into using Astro by Astronomer is that it is a really good tool. Everyone can use it and they can try using it first on their personal laptops and then, if they think that it is useful, they can use it in their day-to-day work. I would rate this product as an 8 out of 10.

In our project, the main use case for Astro by Astronomer is to manage the DAGs, utilizing DBT workflows as well as PySpark running on EKS for triggers and scheduling the DAGs, ensuring our delta loads are scheduled in Astro by Astronomer.

A specific example of how I use Astro by Astronomer in my workflow is that daily there is a file dumped in our landing zone S3. We have the PySpark code to load from the S3 landing zone to our Snowflake landing zone, and then from landing to raw and raw to curated, featuring DAGs such as the EKS DAG which runs the PySpark. We trigger that DAG, and once that job is completed, we trigger the raw pipeline and then trigger the curated pipeline, allowing this delta pipeline to run daily and automate my delta loads.

The best features Astro by Astronomer offers are the scheduling, the monitoring, and especially the APIs, which provide flexibility. I can create custom dashboards through the API as well as implement scheduling, and it offers a great UI on top of Airflow, which is an open-source system. The self-hosting aspect is beneficial because I do not have to manage the Astronomer or Airflow instances myself and can spin them up very easily without any overhead, making it a user-friendly SaaS platform.

Day-to-day, I find myself relying most on the scheduling and the SaaS feature, as managing an Airflow instance is quite difficult. If the server crashes or there are machine problems, I would normally have to deal with the infrastructure myself, but with Astro by Astronomer, I do not have to worry about that and can spin up any deployment quickly. Since scheduling is a default feature of Airflow, if you consider Airflow, scheduling is essential, but for Astro by Astronomer, I emphasize the SaaS part, enabling me to create deployments on the go without any hassle.

Astro by Astronomer has positively impacted my organization because it is reliable, allowing me to depend on it for managing the Airflow instances along with the infrastructure and error handling.

I can share specific outcomes indicating that we have saved significant time, as hosting an Airflow instance on a server is a time-consuming task that we can avoid entirely. In just five minutes, I can spin up an instance in Astro by Astronomer, and the error handling is time-efficient since errors are notified instantly. Without Astro by Astronomer, someone would need to manually check for errors, which requires going through a separate integration, while in Astro by Astronomer, it works out of the box.

I believe there is a need for improvement in Astro by Astronomer, especially regarding the strict limitation of 10 DBT deployments. Increasing that limit would be very helpful since it currently acts as a bottleneck, and I cannot increase it even if I want to.

The main reasons I have not rated Astro by Astronomer higher or lower include the hard limit of 10 DBT deployments, which I find limiting, and the fact that the UI could be better, particularly the UX part. Improvements can be made in how easy it is to interact with it. Overall, I rate it a seven.

I have been using Astro by Astronomer for around two and a half years.

Regarding the UI, integrations, and how it handles notifications or error tracking, I appreciate that a separate smart flow is needed for that. The error messages are useful since any encountered error is automatically sent to me via email, indicating that the integration is quite good.

My advice to others considering using Astro by Astronomer is that it is great, flexible, and very useful. Because it operates as a SaaS, we do not have to deal with deployment, so I highly recommend it.

In terms of Astro by Astronomer's AI capabilities, I think the governance and security are commendable since it is integrated with good governance practices and does not allow data to flow out of the clusters, indicating strong governance and security.

Regarding the accuracy and reliability of output from Astro by Astronomer's AI capabilities, I find it accurate and reliable, although sometimes the logs disappear, so I would suggest improving that aspect. I rate Astro by Astronomer a seven overall.

My main use case for Astro by Astronomer is to create DAGs to run queries in AWS Athena or to instance jobs in EMR, Elastic MapReduce. Specifically, we use an EC2 server where we need to create jobs and take Parquet files to consolidate small files, so we use this to reduce costs with the API get object in S3.

Astro by Astronomer has positively impacted my organization because it is open source code, so we can use it in AWS, Azure, GCP, or on servers that we have on-premise, ensuring we can use it in every situation. Additionally, since it is open source, we do not have to pay any license, which I think is the best feature, along with the ability to control DAGs and create situations using CronJobs.

After adopting Astro by Astronomer, we reduced a lot of time and improved security using DAGs to work queries and run queries in Athena. Users can create the query and run the DAG to execute in Athena, improving security because not everyone has access to AWS Athena and reducing costs.

The best feature Astro by Astronomer offers is the user interface, which is very good, and the logs that we can access in the user interface are very helpful to debug problems.

What I appreciate about the user interface is that it is very friendly, very easy to navigate, and very easy to use.

I think Astro by Astronomer can be improved for newcomers, especially regarding the installation, making it more friendly for Windows users, as we use WSL update. I think this is an opportunity to develop something better for Windows.

I really do not work with governance and security in Astro by Astronomer, but we do not have any problems with it. We use a server, and we have never had any problems, so I think it is acceptable, even though I am not a specialist in security.

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

Astro by Astronomer is stable.

I think Astro by Astronomer's scalability works well. We can run a lot of concurrent jobs or concurrent queries without having any problems, so I think it is acceptable.

The customer support for Astro by Astronomer is very good because sometimes I try to reach even the developer on GitHub, and they respond. I do not have any problems with support. I think it works well and is very friendly.

We do not use a different solution. Sometimes we use Step Functions in Amazon Web Services, but I think Astro by Astronomer is the best solution.

I did not choose Astro by Astronomer; my company chose it, and I do not know if there were other options at that time because I was not working at the company then.

We did not purchase Astro by Astronomer through the AWS Marketplace. We use an EC2 server in a virtual machine and installed it on the EC2 server.

I think we see a return on investment in time, as we saved a lot of time running queries, initiating EMR jobs, and deploying jobs in EMR. Afterward, we can disable the job and terminate the EMR cluster. I think this saves a lot of time in the process to deploy and terminate the instance.

I do not work with pricing, but I think the cost is very cheap. The pricing is not something I know about, as I do not handle pricing, license, and security in my company. I am just a tech person.

My advice for others looking into using Astro by Astronomer is to see the opportunities and try to test new solutions because there are many opportunities to work with this tool. I would rate this product a 9 out of 10.

A specific example of how Astro by Astronomer has made one of these processes easier in practice is that I developed a flow for the lifecycle of Machine Learning models for the search engine, in which the process automatically validates the latest trained models, executes integration tests, and if those are successful, takes care of moving the models to the production area and performing their automatic deployment, covering each step of the full Machine Learning lifecycle flow while maintaining risk mitigation and traceability of each step.

In addition, many of the ETLs are connected to Slack, which allows us to have traceability of when a task fails and to maintain good monitoring.

Astro by Astronomer has positively impacted my organization in the sense that when we have problems with tasks, Kubernetes pods, and other infrastructure components, we already have quite a bit of clarity about the reasons or failures that may be occurring.

I have seen improvements in response times and error reduction since using Astro by Astronomer. Previously, the costs we had for our model training, ETL processes, and batch transformations were totally invisible and did not give us the visibility to know how many resources and how much money we were spending on them. Now, with the visibility that the Astronomer team gives us, we have a way to track excessive resource usage to optimize it and reduce costs.

To add to the platform's features and about the support we receive, since we switched to Astro by Astronomer, we have not had serious problems regarding the management of our instances.

My experience with customer support has been quite pleasant, as we always get a response.

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.

We are managing and maintaining pipelines for our production data warehouse. We have data in Salesforce, which we are ingesting to the data warehouse using Airbyte, and Astro by Astronomer is our orchestrator to run the pipeline every day at a specific time.

Astro by Astronomer is our main scheduler, and we can have a good overview of our pipelines, which we have 70 or 80 each day. It is very useful to see the graph of the pipelines and how they are looking every day and every morning, and we have a clear view of our performance.

The scheduler itself in Astro by Astronomer is very useful. The dev mode is really nice for us as developers, and the option to test our pipelines in CLI is beneficial.

Every time we want to prepare a scheduler in Astro by Astronomer, we can simply write code, write the DAG, and then test it locally. We can open the scheduler UI window in the local host and then test the pipeline to see if everything is running, if all the environmental variables are set and nothing is missing. This is really useful in comparison to our previous tools that we were using.

With Astro by Astronomer, we are paying less than before for MWAA. We are paying for the usage. We can offer Astro by Astronomer usage for other teams which did not have access to our AWS account, and we manage access for them, so it is also a bit easier.

Collaboration is definitely a valuable feature. As I mentioned, we can offer access to other teams. The billing is approximately twice less than before, so it is very useful.

I would recommend making it easier to read the logs in Astro by Astronomer because the logs are pretty hard to follow, especially for the pipelines that are processing much data, such as our DBT, where we have issues finding something in the logs. We can download the logs and then look at the file itself, but otherwise in the UI, it is pretty hard to follow.

I have been using Astro by Astronomer for one to two years after migration from MWAA from Amazon.

Astro by Astronomer is stable.

Regarding Astro by Astronomer's scalability, we are not using so much to have a problem with scalability, but we were able to set everything that we need in terms of scalability in our small circle of pipelines, which I mean to be 80 or something like that.

I have not had contact with them regarding customer support, but my manager had contact with them and it was fine.

MWAA from AWS is the solution we previously used. We switched because of the costs and we wanted to have the dev mode.

The initial setup took seven days.

We have realized money saved on costs with Astro by Astronomer, but there are no other findings.

I have no issues with Astro by Astronomer regarding setup costs or security capabilities, so I cannot think of anything wrong. We are managing access and it is pretty secure. I do not have any issues with that.

Only staying on AWS is the option I evaluated before choosing Astro by Astronomer.

Overall, Astro by Astronomer is a good product. There are still some missing fields, and I could maybe think of two or three additionally, but it is not a perfect tool. Seven is a fair rating.

I have not used Astro by Astronomer's AI capabilities, so I cannot answer that question.

I recommend that others test Astro by Astronomer on their own and take some free certifications because they are available and useful to start with the product. My overall review rating for Astro by Astronomer is seven out of ten.

I conducted this exploration in my personal space and wanted to become aware of what features Astro by Astronomer has, so there is nothing to do with my company.

I wanted to understand Astro by Astronomer itself, so there was no specific use case. I needed to understand what features it has and the feature I really appreciated is the migration capability while upgrading Airflow version two to version three. Astro by Astronomer has amazing features that allow you to accomplish everything with just a few command prompt terminal codes. That is one of the cool features that provided me significant value. Apart from that, the rest of my exploration was exploratory in nature.

I was attempting to manually migrate from upgrading Airflow two to Airflow three, and it was a real nightmare. The libraries moved to different paths, so all the jobs needed to be rebuilt and all the DAGs needed to be reworked. These kinds of challenges are handled with ease using Astro by Astronomer.

I would definitely say the migration capabilities save huge amounts of time and effort. This will not be a small win; it will be a massive win.

For a small scale production environment, it might save you two to three days instead of running DAG after DAG with manual upgrades or manual code editing. Astro by Astronomer will complete the work quite quickly, within an hour or so.

Astro by Astronomer's CLI might be a little challenging, and having the CLI commands along with the documentation more readily available would be great.

I have not encountered any stability issues.

Resources are available, customer education is solid, and the customer journey is very nice with good follow-ups. Astro by Astronomer hosts regular sessions, which is another feature I appreciated.

Curiosity made me think about Astro by Astronomer, but I do not have any burning issues otherwise. Broad-grade code is currently running on my Cloud Composer and has been for the last five years without any issues. So far, everything has been good and smooth.

If you need advanced features and pain points addressed, go for Astro by Astronomer. If you are running small scale broad-grade code with fixed pipelines and you are just running maintenance while looking at how the pipelines operate the same way, then stick with your current solution. If you are working with heavy code or need well-orchestrated pipelines, then consider Astro by Astronomer.

All the features are readily available and Astro by Astronomer's user interface itself is quite intuitive. All developer problems and engineer problems are addressed by them very quickly. I would rate this review a 7 out of 10.