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## AWS Migration Challenges and How to Overcome Them ### AWS Challenge 1: Legacy Application Compatibility Migrating legacy applications to AWS can face compatibility issues due to outdated dependencies and configurations. Solution: Conduct a thorough application assessment, update dependencies, and refactor code where necessary. Utilize AWS migration tools for compatibility checks and implement gradual migrations to minimize disruptions. ### AWS Challenge 2: Data Migration Complexity Transferring large volumes of data to AWS can be complex, leading to downtime and potential data integrity issues. It is one of the serious AWS migration challenges. Solution: Employ AWS DataSync or Snowball for efficient data migration. Prioritize data based on criticality and use incremental migration strategies to reduce downtime. … ## Frequently Asked Questions (FAQs) ### 1. What was the biggest AWS challenge 2025 working with AWS? - Learning Curve: The vast range of AWS services can be initially daunting, requiring time and effort to become proficient. - Service Complexity: The intricacies of individual services, configurations, and interactions demand careful understanding. - Cost Management: Effectively managing costs and preventing unexpected expenses can be challenging. - Continuous Updates: Keeping up with AWS updates and new features requires ongoing commitment and adaptation. … ### 3. What is not a benefit of AWS? - Potential Complexity: The multitude of services may lead to complexity, especially for beginners. - Cost Management Challenges: While cost-effective, understanding and optimizing costs can be intricate. - Security Responsibilities: Shared responsibility for security means customers must manage aspects like data encryption and access controls. - Learning Curve: The initial learning curve for AWS services might be steep for some users.

In 2025, we delivered a comprehensive set of major enhancements focused on three core areas: reducing dev-test cycle through early validation, improving deployment safety with improved configuration drift management, and integrating IaC context to AI-powered development tools. These launches address common pain points in infrastructure development workflows, from catching deployment errors before resource provisioning to managing configuration drift systematically. The features span the entire development lifecycle, from template authoring in your IDE to multi-account deployments at scale. … ... CloudFormation now validates your templates during change set (preview of infrastructure changes before deployment) creation, catching common deployment errors before resource provisioning begins. The validation checks for invalid property syntax, resource name conflicts with existing resources in your account, and S3 bucket emptiness constraints on delete operations. **Figure 1: Pre-deployment validations view** ... Configuration drift occurs when infrastructure managed by CloudFormation is modified through the AWS Console, SDK, or CLI. Drift-aware change sets address this challenge by providing a three-way comparison between your new template, last-deployed template, and actual infrastructure state. **Figure 4: Examine the drift-aware change set to see the dangerous memory reduction that would occur**

{ts:488} really face it some of these manual tasks are not even things we actually enjoy doing as developers modernization {ts:495} transformation I'm sure some of you wake up every day say wow I want to modernize I want to transform but sometimes it {ts:501} just sounds kind of like you're just kind of stepping through some mud and leaving some some Footprints and fossils

# 5 common pain points with AWS Lambda ## Starting without a framework One of the biggest mistakes made when creating a serverless architecture using AWS Lambda is writing functions without a framework. There are multiple frameworks available to deploy and wire up Lambda functions to the events which will be triggering them. These frameworks handle the deployment of individual Lambda functions, setup of API Gateway to pass through HTTP events or a Cloudwatch Scheduled Event to trigger a Lambda on a cron-job. … ## Large bundle sizes When starting out with AWS Lambda large bundle sizes aren’t typically a problem, however when your architecture grows and you start to amass a large number of Lambda functions max bundle size starts to become an issue. There is a default deployment size limit of 50MB. To get around this you can utilise webpack to bundle each entrypoint/Lambda into its own file. Another added benefit of individual bundling is faster deployment and spin up of individual functions meaning you can update and deploy just a single function instead of the entire architecture. … ## API Gateway rejects your response It’s possible for API Gateway to return an error 502 Bad Gateway, which states that the response the Gateway received from your Lambda is malformed. The biggest cause for this that I encountered when getting started with AWS Lambda is the requirement to return a string as the body of the request. It’s a good idea to create helper functions to format your responses and add any extra appropriate headers like CORS.

{ts:549} but if your requirements are too specific not supported unlikely to be supported {ts:555} within the time frame that you need maybe you need to compose your own service not for everything but for this {ts:561} particular thing and to sort of illustrate that i'm going to borrow and adapt some slides that adrian

If I need to look up the docs for every single API, or even just **feel** like I might have to look them up, it detracts from my actual work: solving business problems. Inconsistent APIs cost more time to implement, and they increase the mental load on developers. But they might also reduce the quality of the applications using them. Worst case scenario, this can lead to a bug in production, with serious time and effort required to fix it. And that cost adds up. … What I found was that there’s no consistency between different AWS services, between related AWS services, and even **within** a single service’s API. Let me give you an example of each. … When we include Kinesis Video Streams (KVS), it gets even worse. This service also has the `StreamARN `and `StreamName` fields, but you can get them through `ListStreams` **and** `DescribeStream`. And for KVS, the creation timestamp is stored under yet another name: `CreationTime`. ### Inconsistency within an AWS service Inconsistency within a service is the worst type of inconsistency because, ostensibly, a single service is maintained by a single team. It’s a real problem for developers. You can make a mental note that when talking to a different API, you might need to use a different syntax. But when various calls within the same service are inconsistent, you can never really rely on muscle memory, ever. One of the more painful examples of this can be found working with REST APIs in API Gateway. This is a complex service, so it can be forgiven for having a complex service API. It has dozens of resources, including API Keys, Methods, Resources, Usage Plans, and Documentation Parts. Each of these resource types can be retrieved in singular form with a request like `GetAuthorizer` and in plural form with a request like `GetAuthorizers`. (Side note: That’s a whole new variation on the `Describe `and `List` standard.) … This `items` versus `item` example can lead to bigger issues. If you’ve seen 20 examples of an `items` response, you’d be forgiven for assuming the 21st also returns `items`. An automated check or test deployment might surface this bug, but it might also miss it. Fixing it after it’s live can cost significant time as well as your reputation. … ### Amazon CloudFront The worst service API I’ve encountered so far is CloudFront. This API is inconsistent compared to other services, is inconsistent within its own domain, and returns responses in such a broken way that it should be considered a bug. First, the method-naming and response format. CloudFront has `**Get**`, **`List`**, **and** `**Describe**` calls. The `List` operations all return a dictionary with an `Items` key, which is a list: … This is bad. This will break any parser because an empty string is not valid JSON. It also means that all of these calls have two different types of output: a dictionary or an empty string. As a developer, it means you have to inspect the result length before passing it on to the parser. You can’t even write a simple try-catch block because then you wouldn’t be able to differentiate between a failing API call and zero results. This is not only bad design, but once again, it’s inconsistent with all the other services that at least offer reliable output. … Despite the problems that inconsistent APIs create for AWS customers, the burden will continue to be on us developers for the foreseeable future.

And the challenge with AWS is not limited to its intricate setup. Cost management presents another hurdle; the service’s pricing structure can be confusing, which can lead to bill shock. Read on for an examination of the factors that make AWS complicated and best practices for simplifying its use. **Key takeaways**: - AWS is a hyperscaler cloud platform with an ecosystem of over 260 services, but its seemingly unintuitive interface and fragmented documentation can create a steep learning curve that can lead to complexity fatigue for developers. - The platform’s usage-based pricing structure and not so clear fees, such as data egress and idle resource charges, can result in unexpected bill shock for businesses without dedicated cost-management teams. … ### An extensive ecosystem of products AWS’s many products make the platform more complex. Businesses must navigate a maze of over 260 services, each with its own set of features, configurations, use cases, and pricing models. For instance, selecting the right type of compute service from options like EC2, Lambda, or ECS requires a nuanced understanding of the workload requirements. The continuous rollout of new features and services further complicates the mastery of the platform, as IT professionals must constantly learn and adapt—in some cases necessitating a need for cloud certifications and dedicated staff to manage an organization’s AWS infrastructure. The challenge extends to integrating these services into a cohesive cloud architecture that delivers a clear cloud ROI. The wrong combination can lead to inefficiencies or reduced performance. At worst, this underlying complexity also affects AWS’s internal operations when it comes to a speedy recovery from an outage. ... The user interface of AWS has garnered criticism for its lack of intuitiveness. Users frequently describe the web console as cumbersome, hindering efficient navigation and management of the extensive suite of AWS services. This complexity is compounded by the fact that AWS offers multiple redundant methods to accomplish even simple tasks, leading to a lack of standardization that can confuse both new and experienced users. For example, deploying a containerized application alone can be done through ECS, EKS, Fargate, App Runner, or Lambda—each with different trade-offs, pricing models, and learning curves. This variability in usage can steepen the learning curve, making the initial stages of AWS adoption daunting, not to mention ongoing maintenance, including effective cloud cost management. Plan to Eat is a meal-planning app that moved from AWS in search of a more approachable infrastructure provider. ... Additionally, the pace at which AWS evolves can make documentation quickly outdated. This leaves users with guides that no longer reflect the current state of services in terms of the most current naming mechanism or user interface elements that correspond to provided screenshots. Furthermore, users have raised concerns about tools and services becoming deprecated (such as Cloud9 IDE, CodeCatalyst, and S3 Select), but their documentation lingers without completely addressing the state of affairs. This lack of structured, up-to-date information forces users to continuously verify the relevance of tutorials and instructions. … These costs, combined with the absence of a specific AWS support response time service level agreement (SLA), can lead to prolonged wait times, particularly for users of lower-tier plans. ### AWS pricing structure Understanding the cost structure of AWS can be daunting. You won’t have to look hard to find online anecdotes about such surprises, prompting a niche for consultancies specializing in AWS billing clarification—in addition to AWS itself capitalizing on the opportunity with its own Professional Services arm. AWS egress charges for data transfers outside AWS or across regions contribute to the complexity and can quickly add up. Users must carefully architect their systems, considering factors like region selection and service usage, to mitigate these costs. Additionally, overlooked charges from idle resources, like EC2 instances, or orphaned snapshots in storage, can accumulate. Other AWS seemingly hidden costs to look out for include hourly NAT Gateway fees and charges for EBS storage and Elastic IPs that persist even after you’ve stopped your instances. Even basic visibility through CloudWatch logs or routine API requests can inflate your invoice, while technical support is pay-to-play, starting at $29 per month. Confused by surprise charges and hard-to-decode cloud invoices? Complex pricing models are a big reason many teams struggle with AWS. Learn how to understand your cloud bill—and what predictable pricing should actually look like. … AWS is notoriously complex, even for experienced developers. The platform offers over 260 services, many with overlapping functionality; fragmented documentation makes it difficult to determine the right approach for common tasks. The management console is dense and unintuitive, and the usage-based pricing structure is opaque enough that unexpected ‘bill shock’ is a common experience—even for teams actively trying to manage costs.

"To create resilient systems, one must remain LLM-agnostic," statedil W, C at Klyo. "The 'best' model for any task is constantly evolving, so the ability to switch to the most effective model without significant re-architecture will be essential for sustained success." … ... In conversations I had, some attendees expressed dissatisfaction with the announcements, suggesting they weren't as groundbreaking as previous keynotes. They felt many updates were reiterations of existing offerings or added minimal value to current products. However, the counterargument was that AWS is essentially feature-complete, and any new additions should provide more incentives for customers to remain loyal.

### Lack of Experience with AWS Services One of the most common challenges faced by AWS developers is a lack of experience with AWS services. With a constantly evolving platform and a wide range of services, it can be overwhelming for developers to keep up with the latest features and best practices. This can lead to inefficient coding, poor performance, and security vulnerabilities in their applications. To address this challenge, developers should invest time in learning about the different AWS services through online courses, documentation, and hands-on practice. ... ### Scalability and Performance Scalability and performance are key considerations for AWS developers, as they need to design applications that can handle varying workloads and maintain high performance under heavy traffic. However, achieving scalability and performance can be challenging, especially for complex applications with multiple dependencies. To address scalability and performance issues, developers should design their applications for scalability by using AWS services such as Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), and Amazon DynamoDB. ... While AWS offers a wealth of benefits for software development, developers may encounter certain challenges that can impact the success of their projects. By addressing common challenges such as lack of experience with AWS services, security concerns, cost management, and scalability and performance issues, developers can optimize their applications for efficiency, security, and cost-effectiveness. Ultimately, overcoming these challenges requires continuous learning, collaboration with experienced AWS professionals, and leveraging the latest tools and best practices offered by AWS. ... **Identity and Access Management (IAM) Vulnerabilities:**Misconfigured IAM roles and permissions can leave AWS environments vulnerable to unauthorized access. Developers must carefully manage user access and permissions to prevent security breaches. **Network Security:**Inadequate network security measures can expose AWS applications to external threats. Developers must configure Virtual Private Clouds (VPCs) and security groups to restrict access to their applications and data. **Data Encryption:**Failure to encrypt sensitive data can result in data leaks and compliance violations. Developers must implement encryption mechanisms to protect data at rest and in transit within their AWS environments. **Compliance and Governance:**Meeting regulatory requirements and compliance standards is a significant challenge for AWS developers. Developers must ensure that their applications comply with industry regulations and follow best practices for governance. … ### Cost Management Challenges One of the most common challenges faced by AWS developers is cost management. AWS offers a pay-as-you-go pricing model, where developers only pay for the resources they use. While this model can be cost-effective for many businesses, it can also lead to unexpected costs if not managed properly. One challenge that developers face is understanding the cost implications of the services they use. With AWS offering over 200 services, it can be overwhelming for developers to keep track of how each service contributes to their overall costs. This lack of visibility can lead to overspending and budget overruns. … Security concerns are always at the forefront of an AWS dev's mind. With all the data floating around in the cloud, one small misstep could spell disaster. Networking issues can really throw a wrench in your plans. Trying to get all your services to talk to each other smoothly can be a real challenge. Managing costs on AWS can be a real chore. It's easy to overspend if you're not careful with your resources. Anyone got tips on keeping those bills down? Error handling in your code can be a real pain in the behind. Sometimes it feels like you're just chasing after bugs all day long. Asynchronous programming on AWS can be tricky to get the hang of. Trying to coordinate all those different services can feel like herding cats. Man, one of the biggest challenges faced by AWS developers is balancing cost and performance. You want to use the best resources for your application, but you also don't want to break the bank. It's a constant struggle to find that sweet spot. I totally get that! And then there's the challenge of managing security on AWS. You have to make sure all your data is encrypted and that your IAM policies are set up correctly. It can be a headache, especially for newbies. Yeah, security is definitely a big one. And let's not forget about scalability. As your app grows, you need to be able to scale up or down quickly to meet demand. It's not always easy to predict, so you need to be prepared. … I think it's a combination of all those things. But personally, I find troubleshooting and debugging to be the most challenging. When something goes wrong in your AWS environment, it can be a real headache trying to figure out what happened and how to fix it. Definitely! And let's not forget about staying up to date with all the latest AWS features and services. The cloud industry is constantly evolving, so you have to constantly be learning and adapting to stay ahead of the game.

My sense is that this is actually not far from how AWS permissions are implemented under the hood, but they completely fail at the user-facing side of this by making it much harder than it needs to be to discover where to define the rules for something (and by extension, where to find the rules currently in effect for it). … and the language is a dumpster, imho > 1. I need a goddamn CLI to run it (versus giving someone a URL they can load in their tenant and have running resources afterward) CloudFormation is the only IaC that supports "running as a URL" and that's only because it's an AWS native solution. And CloudFormation is a hell of a lot more painful to write and slower to iterate on. So you're not any better off for using CF. … Because it's not straightforward. 1) You need to have general knowledge of AWS services and their strong and weak points to be able to choose the optimal one for the task, 2) you need to have good knowledge of the chosen service (like DynamoDB or Step Functions) to be able to use it optimally; being mediocre at it is often not enough, 3) local testing is often a challenge or plain impossible, you often have to do all testing on a dev account on AWS infra. … You speak about Step Functions as an efficient and cost effective service from AWS, and I must admit that it's one that I avoid as much as I can... Given the absolute mess that it is to setup/maintain, and that you completely lock yourself in AWS with this, I never pick it to do anything. ... Using all the bells and whistles of a provider and being locked-in is one thing. But the other big issue is that, as service providers, they can (and some of them did more often than not) stop providing some services or changing them in a way that forces you to make big changes in your app to keep it running on this service. … So you try to deploy your change, IAM pops an error or two, you try to figure out what permissions you need to add to the bootstrap script, you run it (fixing it when it breaks at this point), you try deploying again, IAM pops another couple of errors, and then you're in a grind cycle which you can't predict the length of - and you need to get to the end of it before you can even test your feature, because fully serverless means you can't run your application locally (and getting management to pay for the pro localstack licence is a dead end). At some point it won't be clear why IAM is complaining, because the error you get makes no sense whatsoever, so at that point it's off to support to find out a day later that ah, yes, you can't use an assumed role just there, it's got to be an actual role, and no, that's not written down anywhere, you've just got to know it, so you need to redesign how you're using the roles completely, and right about this point is when I usually want to buy a farm, raise goats, and get way too into oil painting, instead of whatever this insane waste of life is. … This only impacts our customers who live far away from the region they're accessing, however, and there's usually a workaround - in Node you can force the network family to be v4 for instance No. They break existing customer expectations. There are heaps of dualstack API endpoints https://docs.aws.amazon.com/general/latest/gr/rande.html#dua... if that's what the client wants. The amazonaws.com domain endpoints did not introduce ipv6/AAAA directly is (mostly) due to access control. For better or worse there are a lot of "v4 centric" IAM statements, like aws:SourceIp, in identity/resource/bucket policies. Introducing a new v6 value is going to break all of those existing policies with either unexpected DENYs or, worse, ALLOWs. Thats a pretty poor customer experience to unexpectedly break your existing infrastructure or compromise your access control intentions.

Python is a popular, high-level programming language that is widely used for web development, scientific computing, data analysis, artificial intelligence, and more. However, despite its popularity and versatility, Python users often encounter several pain points when working with the language. In this blog post, we’ll explore some of the most common pain points and provide tips on how to solve them. - Performance: Python is an interpreted language, which means that it can be slower than compiled languages like C or C++. However, there are several ways to improve performance, such as using NumPy or PyPy, or writing performance-critical code in C or C++ and integrating it with Python. - Memory management: Python automatically manages memory, but this can sometimes lead to memory leaks. To avoid memory leaks, make sure to close files, sockets, and other resources when you’re done with them. You can also use memory profilers to identify areas of your code that are consuming large amounts of memory. … - Code readability: Python is known for its readability, but it’s easy to write code that’s difficult to read. To improve code readability, follow PEP 8 style guide, use descriptive variable names, and write docstrings to document your code. In conclusion, Python is a powerful and versatile language that can be used for a wide range of applications. However, users may encounter several pain points when working with Python, including performance, memory management, debugging, package management, and code readability. By following the tips outlined in this post, you can overcome these pain points and make the most of Python.

I strongly suspect that devs' satisfaction with Python is strongly correlated with the size of the codebase they're working on. Generally people using Python for one-off projects or self-contained tools tend to be pretty happy. People stuck in sprawling enterprise codebases, with O(million) lines of code to wrangle, seem almost universally miserable with the language. What I've observed a lot is that many startups or greenfield projects start with Python to get an MVP out the door as fast as possible. Then as the scope of the software expands they feel increasingly bogged down and trapped in the language. reissbaker on Nov 13, 2020 ... I have used that library on multiple projects for my job. It makes the code run about 50% slower, on average, because all the type checking is done at run-time. I am OK with the slow down because I don't use Python when I need speed. My "developer speed" was greatly improved with stricter types. … If we are being realistic, most of the time 80% of python programs are spend waiting on network. Granted, python is not overly fast, but then most of the time your bottleneck is the developer not the language. > no concurrency Yes, this is a pain. I would really like some non GIL based threading. However its not really been that much of a problem. multiprocessing Queues are useful here, if limited. Failing that, make more processes and use an rpc system. > typing as second-class citizens The annotation is under developed. being reliant on dataclass libraries to enforce typing is a bit poop. > People stuck in sprawling enterprise codebases, with O(million) lines of code to wrangle, seem almost universally miserable with the language. I work with a _massive_ monorepo. Python isn't the problem, its programmer being "clever" or making needless abstractions of abstractions. None of that is python's issues, its egotistical programmer not wanting to read other people's (un documented) code. And not wanting to spend time make other people's code better. … As for processing scalability - Python is OK, but it's considerably hampered by BDFL's own opinions. The result is a few third party libraries that implement parallelism in their own way. That functionality should be integral to the standard library already. The worst part is lack of standard API for data sharing between processes. > packaging Python's packaging issues only start with package management. Setuptools is a wholly mess of a system, that literally gave me headaches for the lack of "expected features". I hate it with every single cell in my body. And then there are systems and libraries, where you literally cannot use docker (Hello PySpark!). … If only for the lack of (strong/static) typing and the relatively underpowered control flow mechanisms (e.g. Python often using exceptions in their stead)... While surely all languages have pain points that show up at scale, Python still has a notable lot of significant ones precisely in this area. >docker, poetry, venv... … Ever wonder why you don't hear endlessly about different ways of doing dependency management in non-Python ecosystems? Because we have tools that actually work, and get on with actually writing programs. It baffles me that Python keeps making new tools and keeps repeating the same mistakes over and over: non-reproducible dependency resolution, excessively tight integration between the language and the build tools, and tools and infrastructure that can't be reused locally. … 1) system packages: almost always out of date for my needs 2) Binary wheels: I actually haven't investigated this much, maybe it will work (and if it does, I'll buy you a drink if we ever meet in person). 3) Building from source: this kinda proves my point about Python having poor dependency management tools if this is a serious response. In general, this would be much further down the rabbit hole than I want to go. … ... Additionally, they are part of a larger application, which is mostly managed by pip, which means that I need both pip and conda which is where things get really, really hairy. I actually blame Google and FB here, as neither of them use standard python dependency management tools, and many of their frameworks bring in the world, thus increasing the risk of breakage. … What I've observed a lot is that many startups or greenfield projects start with Python to get an MVP out the door as fast as possible. Then as the scope of the software expands they feel increasingly bogged down and trapped in the language.