AWS Step Functions Reviews

My main use case for AWS Step Functions is in our data platform where we are trying to generate alerts. For generating the alerts, we are getting the data on a daily basis. This is mainly for the aviation sector. Once we get this data, we have multiple Athena queries that are orchestrated using AWS Step Functions. We have data that is segregated across multiple layers. AWS Step Functions help us execute those queries either in a parallel manner or in a map state, or one after the other. If a layer is completed, then it goes to the other layer. It orchestrates, and it also helps us with the catch state. If it faces an error, then we generate the alerts via webhooks or emails. AWS Step Functions sit as a core, which orchestrates all of these queries for us, and once we have the final data added up, we generate the alerts.

A specific example of a workflow where AWS Step Functions helped me orchestrate those Athena queries and generate alerts involves an aviation-based client for whom we are trying to get the data from the source and ingest it into our S3 bucket that is queried by Athena. This data pipeline is orchestrated via AWS Step Functions where we take these Athena queries again in an orchestrated state across multiple parallel layers or one after the other. In that way, AWS Step Functions help maintain a complete workflow that is work proof. It is robust in nature and helps us get the errors, so if any of the queries fail, our complete workflow, the state machine, completely comes to a halt and error notifications are generated. If not, it successfully completes and we then move on to the next step.

The best features AWS Step Functions offers include the ability to deploy it via code. It helps us orchestrate anything that we want. Even if we have Glue jobs for another use case or Lambda that we are currently looking forward to, we are using Lambda to take DynamoDB data for real-time sales monitoring, then it goes to our S3 bucket. We have multiple workflows that are orchestrated by AWS Step Functions, and at the core, the main functionality is based on different frameworks. We have an Athena complete workflow, Glue job-based workflow, and a separate Lambda-based workflow. All of these are separately handled in AWS Step Functions. This is a good point and again with the inbuilt facilities of it, including how we can construct those workflows and add up a catch state or roll back or do multiple iterations using the Map state, that is very helpful for us.

The Map state feature has specifically helped my team create a rollback state of the data since we were dropping some of our data assets. In case we want them back, we just wanted to do this. We took a particular layer and provided the data list, including the tables list, and using the Map state, we iterated over this list and one by one we did the backups of this data at the concurrency of five tables in a single execution. The Catch state, on the other hand, allows us to identify any error immediately. If we encounter an error, the Catch state immediately picks it up and we integrated webhooks or email notifications. That is how we are able to identify immediately if we face any error.

At the core of our data engineering platform, AWS Step Functions sit at the topmost core of whatever pipelines we have. It is a main orchestration tool for us and helps us save costs. It is a key component of our engineering platform.

I think AWS Step Functions is good, but the UI in the console can be much more user-friendly. It gives three tabs on the same screen in a vertical manner. I would suggest providing a full-page diagram of your state machine. If you want to do anything else, you can just scroll down instead of having three sub-pages in the same direction vertically. Do not stack up in the same page view; rather keep it upside down, in a horizontal manner, or allow us to scroll through it.

I have been using AWS Step Functions for about the last six months.

AWS Step Functions is stable.

AWS Step Functions' scalability is quite good. We have workloads that may differ based on incoming data, and we did not face any lag or job failures because of AWS Step Functions.

I have not interacted with customer support yet because we did not require any customer support since AWS Step Functions is a good service. We did not face any issues.

From the beginning, we have used AWS Step Functions only.

Regarding my experience with pricing, it is good. I believe it should be free. The setup cost was good for us. We just had to define our logic using the AWS CDK, or now we are going with Terraform, which makes it much easier. I think the licensing is already quite well with AWS.

We have seen a return on investment in terms of time saved. If we want to maintain complex workflows, we can get a high-level overview of what we want to design quickly and then implement it via code using Python and deploy it via infrastructure as code using CDK or Terraform to get a basic outline map of how we want to have it laid out in final production. The UI console helps significantly in saving time.

AWS Step Functions helps us save costs as it is a self-managed service and is serverless. We look out for the inbuilt services that are orchestrated via AWS Step Functions, and we only pay for that. In that way, the orchestration service for us is basically free. What we pay for is the Athena queries that we have or the Lambda executions that we have. In that way, the orchestration cost is saved by us and we just pay for the main operations and the execution costs we incur.

We did not think about any other options before choosing AWS Step Functions because the architect who designed the whole data platform chose AWS Step Functions as the best option. The other consideration was AWS Glue for orchestration, but the costs were relatively high.

I rate AWS Step Functions a nine out of ten on a scale of one to ten. I chose a nine out of ten because it is basically everything that we are really looking for. It is a great service and a great asset to our operations. My advice to others looking into using AWS Step Functions is that it is a good tool, and I recommend it. We purchased AWS Step Functions through the AWS Marketplace.

Regarding AWS Step Functions' AI capabilities, I think the governance and security are well-managed as we operate within a VPC and have security groups in place. In that way, we are secured in terms of data security and operational security. Governance is also preserved as we follow GDPR rules everywhere, and AWS Step Functions does not have anything built into it that serves us; rather the VPC helps in this aspect. We do not use the AI capabilities of AWS Step Functions. I rate AWS Step Functions a nine out of ten overall.

My customer's usual use cases for AWS Step Functions that I've been working with include orchestration, flows, diagram creation, and creating a flow for multiple Glue jobs to run a single pipeline.

The most valuable features or capabilities of AWS Step Functions that I have found include the orchestration functionality, which is very useful, and this is their main functionality.

The orchestration functionality of AWS Step Functions is valuable because it is easy to integrate all the AWS functionality inside the Step Functions.

The automatic scaling features of AWS Step Functions are beneficial because it automatically runs fast and operates with previous context. For example, if I'm running multiple flows inside one Step Function, it can run context-wise and allows easy result sharing from one place to another.

AWS Step Functions' fault tolerance capabilities can be improved, but if managed well in orchestration, then the impact is minimal. However, the product-wise capabilities could improve.

Optimization regarding the fault tolerant capabilities of AWS Step Functions needs improvement.

I do not have any other areas of AWS Step Functions I would like to see improved except for the fault tolerance aspect, as the failure and success aspects can be improved in how we implement it in the flow.

I have been working with AWS Step Functions for the last two years.

My impression of the stability and reliability of AWS Step Functions is excellent, rating it nine out of ten. I never faced any faults or issues due to its stability with AWS, making it very reliable with no downtime and 100% reliability.

I would rate AWS Step Functions a six in scalability.

There isn't much need for scalability when creating orchestration in AWS Step Functions, which explains the rating of six. It can have scalability, but it's not crucial in this context.

I have not interacted with the technical support and customer service of AWS Step Functions, so I didn't have any connection with the technical support team.

The initial setup and deployment of AWS Step Functions are always straightforward, with nothing requiring multiple steps.

I use the Visual Workflow Editor feature of AWS Step Functions.

I mainly work through code and then improve it through the visual aspect. There are two ways to do orchestration: through code and through visual. My main task is to develop flows.

I have only implemented AWS Step Functions with AWS services, not with any third-party tools.

The integration between AWS Step Functions and other AWS services is excellent, working seamlessly with EventBridge, Glue jobs, and databases.

I have not encountered any challenges with customers regarding AWS Step Functions that needed me to find a workaround.

The pricing of AWS Step Functions is moderate and not particularly costly.

AWS Step Functions is affordable for small, medium, and enterprise businesses as an orchestration tool.

I have not used any documentation, manuals, or guides for AWS Step Functions as it's very simple to implement. When help is needed, I consult Stack Overflow or AI for commands.

Overall rating for AWS Step Functions: 9 out of 10.

Amazon Web Services (AWS)

I use Step Functions for orchestrating several application workflows. Specifically, I often use Step Functions to automate processes requiring multiple steps and for application orchestration. I focus on integrating with multiple AWS services and managing automations.

Step Functions provide seamless integration with AWS services, which enhances the speed of application development. The JSON app launched recently allows us to define data execution more easily. 

The Standard Workflows feature includes error replay capabilities, which are crucial for efficient error management. The Amazon State Language (ASL) in JSON format facilitates workflow automation and accelerates the deployment of Step Functions.

One area for improvement is the payload size. Currently, I sometimes have to save data as a file since I cannot pass it within Step Functions, necessitating caching in processes. Increasing the payload size would be beneficial.

I have been working with Step Functions for several years, using it more intensively in the last two years across various projects.

Step Functions offer high stability as a regional service that spans across multiple availability zones. If one zone fails, others handle the demand, ensuring high availability.

Step Functions have automatic scalability. They can handle multiple simultaneous requests effectively, starting separate Step Functions for each request, making it highly scalable.

I use enterprise support, which is excellent, providing responses within fifteen minutes. For business support, interaction is only through chat or the web, which limits direct conversation but still maintains high-quality service.

Positive

The initial setup can be rated as an eight on a ten-point scale. Understanding ASL is essential for an efficient setup, and familiarity with it is crucial to face fewer challenges.

Pricing for Step Functions is very cheap. On a scale from one to ten, I rate it as three in terms of costs, with one being the most affordable. It is cost-effective.

Overall, I would rate AWS Step Functions at least nine out of ten. 

The technology is robust, but improving the payload size could enhance the product's utility further.

Public Cloud

Amazon Web Services (AWS)

The primary use case was the execution of scripts and data migration related to data lakes. We were using Python and other AWS Step Functions.

AWS Step Functions provided a good flow for tasks to be executed in data lake scenarios. It was particularly valuable for its integration capabilities, allowing for execution of scripts and data migration.

There is room for improvement in terms of integration with other products. It would benefit from more integration with different applications or services.

The setup of AWS Step Functions was pretty straightforward.

AWS Step Functions was stable, and there were no problems that I can recall.

The project was just an initial project and didn't require scaling, so I cannot attest to its scalability.

AWS technical support is very good, with an eight out of ten. The speed of reply and the content of their responses usually solve the problems that arise.

Positive

The initial setup was straightforward.

I am not familiar with the pricing as most of my projects have environments from the customer side, and the contract-related issues are not my responsibility.

I know a little about Azure's offerings but have not worked deeply with them to compare with AWS Step Functions.

I would recommend AWS Step Functions to others. Overall, I rate AWS Step Functions an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

AWS Step Functions is a useful tool for orchestration, particularly when using various AWS services. It helps create workflows and manage the order between services like Lambda and Glue jobs.

It provides advanced workflows that can save time and make processes more resilient and efficient, reducing delays and improving consistency.

AWS Step Functions offers advanced workflows that save time and enhance efficiency by reducing delays and ensuring consistent orchestration among various services. 

It provides a graphical design interface that allows easy configuration, which improves process efficiency by managing workflows and ensuring there are no inconsistencies or delays.

It is difficult to suggest improvements at the moment. No critical issues or pain points come to mind immediately.

I have been working with AWS Step Functions for about three or four years.

I have not needed critical support from AWS for Step Functions.

Positive

The solution is similar in use to Airflow for orchestration across AWS services.

AWS Step Functions is serverless and can be configured manually using JSON files or via an interface, making it straightforward like other AWS services.

The cost is average, provided it is configured correctly. AWS charges for active usage periods.

It's comparable to Airflow in terms of orchestration capabilities.

Overall, I rate Step Functions nine out of ten. Such ratings are subjective, and I generally do not rate any service a ten out of ten.

Public Cloud

Amazon Web Services (AWS)

I worked with Step Functions about a year ago for six months. Currently, I don't have any use cases for Step Functions. But overall, I was pretty amazed with it. It has great integration with many AWS services, and if you use it correctly, it could save you a ton of money. 

Again, it leverages AWS serverless features. If you want to create a workflow to call one Lambda function after another, and other serverless features, it could save you a ton of money. That's for sure.

One use case was running OCR on PDFs for regulatory compliance. We had two options: launch an EC2 instance with OCR or use Lambda. But we didn't need OCR running all day, just at specific times. Using EC2 instances for OCR operations was pretty expensive. Plus, scalability was an issue if multiple users were using the model. So we shifted it to Lambda and Step Functions.

We dropped our OCR infrastructure cost by 90% due to the efficiency. We paid 90% less for our infrastructure cost by using Step Functions for our OCR processes.

There are two main features I like. You can use the UI or write basic JSON to define the workflow. If you write JSON, it converts it into a visual workflow in the UI. This allows you to visualize what's happening. 

When you get an error, it's easy to find in any large workflow, and debugging is also pretty easy. I like their UI, to be honest. It has the best profit UI.

If AWS Step Functions keeps adding more integrations, it would be even better. 

Otherwise, the UI is great, the functionality is great, and everything is great. AWS Step Functions already has many integrations, but adding more is always good for enterprises.

I have experience with this product. I have been using it for five to six months. 

It is a stable solution. I would rate it a ten out of ten. 

It's very, very scalable. I'd give it a ten out of ten for scalability. 

We implemented Step Functions for one of our clients, and it's one of our biggest clients. So, around three to four thousand users.

I haven't called tech support for Step Functions specifically. But overall, it has the same tech support as AWS. AWS tech support is pretty good, but pretty expensive as well.

If you're new to AWS, then the initial setup seems very difficult. Problem-solving and creating a Step Function to solve those problems is also a difficult task. You just need to spend two to three days to become familiar with how things work. Create some test functions. Two to three days are enough to get comfortable with this application.

We dropped our OCR infrastructure cost by 90% due to the efficiency. We paid 90% less for our infrastructure cost by using Step Functions for our OCR processes.

I would rate my experience with this product a ten out of ten. Step Functions obviously cannot solve every problem statement. But it makes sense for a wide range of use cases. Everything has some limitations, but Step Functions has done great. 

I recommend that others run some test programs first, then move to their actual use case. If they spend two or three days exploring Step Functions, they will get a better grasp of it and ultimately be able to use it for their actual use case.

I was the second member to join a team tasked with creating a proof of concept. My responsibility was to demonstrate how to orchestrate microservices effectively using AWS Step Functions. Essentially, there are two approaches to orchestrating microservices within AWS: one involves using Step Functions as a master workflow to coordinate various microservices, while the other entails integrating the Amazon Java SDK into your products and using TechTokie to ensure smooth orchestration flow.

The biggest benefit is that it allows us to work with complex systems in a user-friendly manner. Having the ability to easily debug and monitor through the GUI adds significant value from my perspective.

The visual workflow interface of Step Functions has significantly enhanced our ability to design and debug state machines. The color-coded visualization, such as yellow and red indicators, is particularly useful, especially in web-based environments. However, when it comes to debugging deeper issues, such as understanding test results, we encountered some challenges. We felt the need for more detailed insights, such as knowing which step is causing issues, especially when dealing with token validation.

One of my favorite features is the AWS Step Functions UI. It's truly amazing to watch the steps in action and easily debug any issues. AWS Step Functions acts as a high-level layer, allowing us to seamlessly integrate with microservices. The UI provides a dashboard-like view that helps me understand the entire system.

There could be better integration with IDEs, such as more seamless access to credentials without needing daily updates. Additionally, it would be helpful to have better visibility into Step Functions, such as being able to view parallel executions within the same Step machine. Improved profiling and logging features would also enhance the management of processes. The interface can sometimes feel limited, as we're unable to see what AWS is running behind the scenes. Having a desktop option might provide more detailed information, especially for teams like ours that need to validate multiple sub-functions concurrently. It would be beneficial to see these functions running in parallel for performance validation purposes.

I have been working with it for two years.

In terms of stability, we place a strong emphasis on reliability, particularly with services like Microsoft. We have strategies in place, managed by our operations team, to ensure the stability of our system. While our focus in development lies more on integrating Microsoft and optimizing its functionality, we understand the importance of having contingency plans in case of failures. Therefore, we ensure there are alternative solutions available, such as automatic restoration or the creation of additional instances, to mitigate risks and maintain uptime.

In terms of scalability, our priority is to provide a solution that can swiftly adapt to surges in demand without incurring high costs. This became especially apparent during the pandemic when remote work became the norm, leading to significant spikes in usage for our applications. We rely on AWS's scalability features to handle such fluctuations in demand effectively. Our infrastructure is designed to support high demand scenarios, and we leverage cloud providers like Amazon AWS to achieve this. Additionally, we implement a dynamic approach to scalability based on real-time demand patterns. We continuously refine and optimize our system to ensure it can handle high demand scenarios seamlessly.

The initial setup was challenging. I encountered some difficulties initially, but now, with more recent experience, it would be smoother. Perhaps a bit more documentation or guidance would have been beneficial back then, but now I feel confident in navigating through it.

For deployment, I delved into the entire Step Functions documentation within the Amazon SDK, which spans nearly one thousand pages. Despite its length, I enjoyed diving into it as I find such features intriguing. My research consumed almost four weeks, but I could have grasped it sooner if not for the extensive AWS ecosystem that required additional study. This initial phase involved learning about AWS services, setting up connections, and establishing rules within the system. Overall, it took me around two to three weeks to get everything up and running smoothly. We've developed a system where we deploy our application across three different environments: development, UAT, and production. Each developer in our team has their own infrastructure, tailored to their needs. This allows us to debug our applications locally using AWS services like AWS Lambda, EventBridge, and S3 buckets for testing purposes. However, in other environments like UAT and production, the deployment responsibility lies with the development team, while we, as developers, focus on coding and debugging. We leverage GitLab pipelines for deployment automation. Overall, our project involves six developers supported by a dedicated DevOps team, along with project owners overseeing various aspects of the project.

Orchestrating microservices with Step Functions provides a high-level abstraction for organizing entire workflows. This approach addresses the challenges that arise when working with multiple microservices simultaneously. While microservices offer benefits like agility and rapid deployment for small teams, managing numerous microservices can lead to complexity and increased costs. Step Functions offer a solution by providing a higher level of abstraction, allowing for easier orchestration of microservices. This abstraction simplifies the management and coordination of microservices, potentially reducing costs and streamlining development efforts.

My advice for anyone considering implementing AWS Step Functions is to start by familiarizing themselves with the user interface rather than attempting to read the entire documentation at once. AWS provides an intuitive and user-friendly UI, making it easy to navigate. However, it's essential to understand the underlying concepts and rationale behind using Step Functions. I recommend taking the time to explore the documentation and understand why this technology exists, its purpose, and the alternatives available. Once you have a clear understanding, start with small practical steps, such as creating a basic workflow and observing the JSON structure generated. Focus on comprehending how data flows through the workflow, as this is the core aspect of Step Functions. Overall, I would rate it nine out of ten.

Public Cloud

Amazon Web Services (AWS)

The major feature of AWS Step Functions is interdependency. Step Functions can determine what action to take next if one step returns a false status based on predefined logic. Step Functions aims to ensure that pipelines or workflows run smoothly by providing a backup function to execute in case of failures.

The integration capability is easy, whereas building state machines is tricky.

The solution's pricing could be cheaper. It is cheaper than Airflow. We cannot directly pass the parameters of any SQL, emails. It is a simple state, but if they provide any coding platform inside, we need to make it more dynamic instead of drag and drop. 

The solution is quite stable. I rate the solution’s stability an eight-point five out of ten.

I rate the solution’s scalability an eight out of ten.

AWS technical support team is good. Certain libraries had a few errors. It gets fixed within five to six hours.

I have used Vonage.

The solution is expensive. It is worth the money. AWS offers event bridges. Another service triggers any job and provides event buses to fasten parameters. For data lakes, event buses are very useful for obtaining keys for each file. Compared to Step Functions, the pricing is much higher, especially when moving from static infrastructure machines. The pricing model should be based on OpenEdge plans.

For Step Functions error handling, one must use function calls and logging for error detection within state machines. Comparatively, AirFlow offers more room for improvement.

It's like drag and drop. If you have to scale it, your diagram becomes too big. AWS suggests using a scripting language for easier measurement.

I rate the initial step an eight out of ten.

I use Amazon Step Functions to visually design and simplify the development of functions, automate various tasks, and coordinate workflows across different AWS services. I also use it to streamline the development of my Azure tool by visually orchestrating Lambda Functions and integrating them with different API services for a more efficient workflow.

The main benefit of Amazon Step Functions for me is the ability to visualize automated processes and logical parameters, enhancing manageability and understanding of program logic.

What I like the most about Amazon Step Functions is how easy it is to use.

In terms of improvement, I would like to see more data transformation features in Amazon Step Functions, like additional operators and logic, to enhance its capabilities in handling and transforming data during workflows.

I have been using Amazon Step Functions for about six months.

I have not experienced any stability issues with this solution.

Amazon Step Functions is definitely scalable enough for my needs.

Before Amazon Step Functions, I used visual workflows without automation, such as Azure Workflow. I chose Amazon Step Functions because of its popularity and our curiosity.

Setting up Amazon Step Functions was straightforward for me. The main challenge was understanding the multitude of connectors and features available, but overall, it was not too difficult once I grasped the various options.

The pricing for Amazon Step Functions is flexible and depends on the usage of the product. It is cheaper than Azure Workflow.

Overall, I would rate Amazon Step Functions as a nine out of ten. It is a great product.

Public Cloud

It is like a workflow to find a sequence of tasks, like in Zapier, Appian, and Camunda.

So, our use case was infrastructure-related orchestration. Not application-related infrastructure.

It was more Amazon API for infrastructure. We were trying some EFS-related stuff and some proprietary APIs from Amazon.

It wasn't easy to understand the licensing model. It's like if you use just a little, it's cheap, but it becomes more expensive as you use more. It's like a hook that ties you inside the Amazon ecosystem. So, it creates a dependency.

I don't like it because it creates a dependency, like Microsoft or other players who want you to stay within their ecosystem as much as possible.

I used this product for a couple of months because it was for an analysis project. So, in that case, it was a practical approach, comparing Lambda and Amazon Step Functions.

It is quite stable.

It's Amazon, it's scalable.

There's no technical support from Amazon unless you are from a bigger organization.

The support is there for critical stuff with it, but not for our particular project.

Zapier and Appian because I was studying them. So, they're suited for small to medium enterprises that mostly use SaaS services. But once you have a decent amount of work volume, they become prohibitively expensive.

However, they're not suited for critical enterprises that require execution in a secure environment. They're just stuck somewhere in the cloud. So, from my perspective, they are not suited.

And when it comes to Camunda, from a technical aspect, from a competitive analysis perspective, it's kind of old school that may not still fit the needs.

The initial setup is fast. There's no setup; it just runs. It's Amazon. You write it and spin it up. It's okay.

I would rate my experience with the initial setup a ten out of ten, where one is difficult and ten is easy.

It is a SaaS platform; it's like just go on a website, and this is not a problem. It's there from the start, and you can spin it up in a few seconds.  

We went for the Step Functions service. The pricing for Step Functions was quite convoluted and difficult to understand due to the numerous small factors involved, such as data transfers and limitations on runtimes. 

Additionally, replacing existing Step Functions tasks was not straightforward. As a result, we opted for a solution that provided clear and understandable outputs, allowing us to effectively monitor and manage our workflows. 

For we went for Lambda function for more decent output and to see what was going on. It was more Amazon API for infrastructure. We were trying some EFS-related stuff and some proprietary APIs from Amazon.

So, pricing depends on what I am doing. If you're consuming two or three things a day, it's fine. It's cheap.

But then there's no price control because Amazon is about, "Let me help you consume as much as possible," so then I can be, like, hell at the end of the month. 

Technically, it's okay, but otherwise, it's Amazon taking all your money if you're not careful.

Overall, I would rate the solution a five out of ten.