Astro by Astronomer Reviews

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.

Private Cloud

Microsoft Azure

My main use case for Astro by Astronomer is to orchestrate data pipelines in the cloud and orchestrate an end-to-end data flow for a Lakehouse or Data Lake for batch processes.

A specific example of how I've used Astro by Astronomer in one of my recent projects is in the flagship project that we have, a framework that is in charge of orchestrating all the different ingestions that are done for the lakes the company has. I orchestrate the ingestion from different sources and then orchestrate the processing to take it to the different layers: bronze, silver, and gold. I would say it is the core of orchestration between layers within the Lakehouse.

It is the core of my day-to-day work. We have more than 50 pipelines running for different lakes across different sources in different lakes, according to domains within the company. Using Airflow is the core to be able to perform all the batch processes and migrations from sources until reaching tables or the semantic layer. It not only orchestrates ingestion; it also orchestrates processing with DBT, modeling with DBT, and processing with EMR and Glue Job.

Among the best features offered by Astro by Astronomer, I would say first that it offers a CLI with a service also in the cloud, this CLI to be able to do specific developments.

Besides the ease of use and quick access from the cloud, I find the CLI especially valuable in Astro by Astronomer. The CLI allows me to do everything in a very automated way. Also being able to deploy in a much faster way and having auditing over the pipelines is valuable. I understand that Astro by Astronomer also has an Astro IDE to be able to build pipelines from the browser with a prompt. That is a tool that I think is quite interesting, though I have not tried it yet.

Astro by Astronomer has had a positive impact on my organization mainly in terms of time savings and error reduction, since I seek to standardize all the processes within my framework. Based on how I handle ingestion, the general DAG that I have is standard for all the lakes that exist in the company.

I can go deeper into how I have measured that time saving and error reduction. Before, each lake worked with the DAGs independently. That implied rework in each of the lakes. Now I have a single framework that is replicated in all the DAGs, in all the lakes. This translates into simply replicating and not having each one be independent. There is substantial time saving there. Also at the error level, when some type of error occurs, due to the experience I have been having within the framework, it is known that it can be reflected and automated for the rest of the lakes or remediated for the rest of the lakes.

I think Astro by Astronomer could be improved by having more clarity in cost topics and how the cost associated with the tool works.

I think there is an aspect of the tool that has caused me some difficulty, which is the scheduler, and I think it could be more intuitive or efficient. The scheduler's latency, which I know is not sub-second and is not suitable for sub-second latency, is more for batch and minutes is recommended; in seconds it can become intensive. In the future, having something a bit more in seconds would be interesting. I also think there is an issue that it tends to be a bit slow; sometimes the tool does not reflect changes quickly, especially when I want to see something refreshed automatically.

I have used Astro by Astronomer for a year, but I have used the entire Airflow suite for more than five years.

I consider Astro by Astronomer to be stable.

I consider the scalability of Astro by Astronomer to be good. It adapts well to my organization's needs when data volumes or users increase.

I would rate the technical support of Astro by Astronomer as unknown since I have not had any relevant experience with the support team. I cannot rate the technical support of Astro by Astronomer because I have not had experience.

I did use another solution before Astro by Astronomer, which was native Airflow.

I decided to switch from native Airflow to Astro by Astronomer because of the official support.

I have not seen a return on investment with Astro by Astronomer. I cannot share any relevant data, such as staff reduction, money savings, or time savings.

Before choosing Astro by Astronomer, I evaluated other options. I evaluated MWAA, AWS's own service.

My advice to other professionals who are considering implementing Astro by Astronomer is to first evaluate the tool, check all the documentation, the CLI, and the courses they have available and then test from there if it is the tool they are looking for. I would rate this product a 7 out of 10.

Public Cloud

Amazon Web Services (AWS)

My main use case for Astro by Astronomer is orchestrating the day-to-day pipelines, including the ingestions, transformations, and alerting and error handling.

One specific example of a pipeline I run with Astro by Astronomer is our data ingestion pipeline, where we use Astronomer-hosted Apache Airflow running in Docker to orchestrate end-to-end workflows. The DAG starts by triggering the ingestion job from multiple sources such as Google Sheets, Airbyte, and some APIs, and we have a custom script for that. We can initiate all these using Airflow, perform data quality checks, execute the transformations, trigger the transformation notebooks in AWS Glue, run validation tasks, and send notifications if any stage fails midway. We also have retry logic in Airflow, which is very effective.

I find that features such as task grouping and the ability to run tasks in parallel are incredibly helpful. Another important feature is XComs, which allows the transfer of data between specific tasks.

In my opinion, one of the best features Astro by Astronomer offers is that it allows us to focus on building data pipelines instead of managing the Airflow infrastructure. This benefit is complemented by the need to manage some Docker instances where Airflow is hosted, including being careful about updates and billing systems and all other networking considerations. Additionally, I have tried the Airflow CLI, which is similar to Astro CLI, allowing me to test the DAGs locally in the Docker environment. The UI has also improved significantly in recent years, making it very clean and user-friendly.

Astro by Astronomer has positively impacted my organization by automating ingestion pipelines, transformations, and error handling, and it has given us relief. Before using Airflow on Astro by Astronomer, we managed dependencies, tracked failures, scheduled, and monitored separately. Now, everything is packaged in Airflow, allowing us to look into a single place. Before, finding the root cause of failures involved checking multiple places, but now it is centralized in Airflow. From a scalability perspective, we have added more ingestions and transformation workflows into the current pipeline, enabling us to scale up easily by dragging and dropping existing tasks.

After using Astro by Astronomer for four years, I can say it has evolved significantly, which is positive; however, improving the debugging experience for complex workflows remains a pain point. While simple pipelines are straightforward, complex workflows become difficult to debug with existing tools. End-to-end tracing and dependency visualization would help identify root causes much more effectively in Airflow. Additionally, the learning curve is becoming more complex for beginners, who may find the multitude of features intimidating.

To enhance Astro by Astronomer, it would be beneficial if it could generate documentation automatically, producing visual documentation for pipelines similar to what we have using DBT.

I have been using Astro by Astronomer for almost four years, since the start of my career.

In my experience, Astro by Astronomer has been stable, especially with the recent version, which has resolved issues that were present in earlier versions.

Astro by Astronomer has handled growth and increased workloads very effectively; during peak hours when multiple pipelines and transformations run, it scales up effectively, allowing us to spin up multiple Docker instances as needed.

I have not used any enterprise solutions before Astro by Astronomer; we previously relied on schedules, cron jobs, and custom scripts to orchestrate the pipeline.

I do not have knowledge of the nitty-gritty details regarding whether we purchased Astro by Astronomer through the AWS Marketplace, as that is handled by our DevOps team.

Although I do not have visibility on the return on investment, I can share that using Astro by Astronomer has significantly improved our productivity by saving time, helping us troubleshoot, and speeding up the ingestion pipeline.

My experience regarding pricing, setup cost, and licensing is limited, as it is handled by the DevOps team and finance team, and I am not involved.

We evaluated other options before choosing Astro by Astronomer, including Dagster among others, but since some team members were already familiar with Airflow, we decided to move forward with it.

I would rate Astro by Astronomer a seven out of ten.

I chose a seven because it has significantly helped us build a scalable orchestration layer, speeding up the ingestion process and onboarding new ingestion sources. While the logging and monitoring features are good, there is always room for improvement, particularly in the documentation and debugging for complex systems, as it becomes challenging in Airflow as complexity increases.

I am not entirely certain about the AI capabilities of Astro by Astronomer, and I am very skeptical about using AI with corporate enterprise data due to concerns about unauthorized access to our data.

I have tried a few prompts with Astro by Astronomer's AI capabilities, and they work effectively; the suggestions are good.

For our public cloud deployments, we primarily use AWS and Azure, with most of our Docker instances hosted in AWS and Azure used for clients and data engineering tasks.

My advice for others looking into using Astro by Astronomer is to give it a try; it is truly useful compared to relying on cron jobs, CLI, and custom scripts since it provides a managed, reliable, and trusted solution used by thousands of developers and data engineers.

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.

Private Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Google

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hybrid Cloud

Amazon Web Services (AWS)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Public Cloud

Other

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I have been using Airflow daily for the last six years as my main product and primary tool to orchestrate data ingestions and process data.

I use Astro by Astronomer to orchestrate data ingestion and to call GCP tools or AWS tools, such as EMR jobs or Dataflow jobs. It also calls Cloud Functions, Lambda, and everything necessary to ingest and process data, including DBT processes.

Currently, I am trying to use Astro by Astronomer for my local development to create a Docker image faster and more easily, allowing me to have my own environment to practice and to use as a sandbox for learning and trying different approaches.

Astro by Astronomer has many integrations out of the box, making it easy to use and to access documentation. Astronomer is a good company to support that.

Astro by Astronomer offers many integrations out of the box with good documentation and a certification workflow.

Astro by Astronomer makes it easier to create retries and send emails, which is very useful.

Astro by Astronomer is consistent and dependable because you have governance over logs and task runs. I can understand the whole workflow for Airflow and use CI/CD to change code and create and modify DAGs.

Astro by Astronomer needs something to debug it, not only to print or log the system but an IDE to put a breakpoint and develop it inside Airflow without waiting for the Airflow job to compile the files and code.

I would like to have the possibility to put a breakpoint inside Airflow and show the variables, the call stack, and anything else on the stack to understand better while developing.

Astro by Astronomer is a complete tool, and with a small change it will improve significantly.

I have been using Airflow daily for the last six years as my main product and primary tool to orchestrate data ingestions and process data.

Astro by Astronomer is very stable.

Astro by Astronomer's scalability is very good. Sometimes we experience performance bottlenecks, depending on the time we are running DAGs simultaneously.

Astro by Astronomer is my first solution for workflow orchestration.

It is a simple product to install quickly, has a lot of documentation, and it is very similar to Airflow. It has the same trunk code.

Astro by Astronomer is a standard tool for data engineering, so I did not analyze any other tools to use.

Because I have been using Astro by Astronomer from the start of the project, it is a handy tool to orchestrate ingestion as a data engineer. Therefore, it has not changed our process but is a part of it.

For me, Astro by Astronomer deserves a rating of 10 because I believe I use it correctly—Airflow as an orchestrator, not as a runner.

If you use Airflow as it was designed, you will experience the full improvements the tool was made for.

Now, the company has installed MWAA instead of using Astro by Astronomer.

I do not have a relevant metric to show, but common sense tells me it is a good tool to have governance and simple maintenance for many processes.

For me, the community around Airflow is growing every day and is very well documented. I am following Marc Lamberti regarding the new features on LinkedIn, and for now, it is sufficient and very useful.

The certification workflow created by Marc Lamberti is great and fantastic because it covers many topics for Airflow and is a good exercise and place to understand Airflow better. It is also good to demonstrate to the community that I am proficient in Airflow.

Public Cloud

Amazon Web Services (AWS)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On-premises

Other