What skills do you think will be most important for cloud infrastructure specialists in the next 3–5 years?

With the constant development of technologies in the field of cloud solutions and the growing interest of companies in them, specialists in this field are increasingly in demand. The migration from private infrastructures to cloud solutions requires experts who can create secure, efficient, and financially sound plans, as well as architectures that do not disrupt the availability of business products.

The market is moving towards more complex roles that combine design, cost optimization, automation, working with multi-cloud and hybrid environments, and security. This is a natural evolution of the specialist profile and reflects the long-term goals of companies in the field of cloud solutions.

Candidates are increasingly valued for soft skills that enable them to translate technical skills in cloud solutions into business value. Presentation skills and the ability to adapt content for non-technical audiences will also find increasing application in the intertwined infrastructure and business solutions in the cloud.

What approaches do you use at Paysafe to develop and retain talent in a team that works with cloud technologies?

At Paysafe, we focus on technical work in which employees see meaning, opportunities for growth and development, a positive culture, and the idea that cloud infrastructure solutions have value equivalent to that of our business products. In modern business applications to end customers, infrastructure plays a key role in ensuring fast, secure, and reliable service for our users.

Beyond the ongoing maintenance of our systems, employees work on business tasks with infrastructure dependencies that provoke technical thinking and experimentation, leading to the construction of architecture and a model for its reliable long-term maintenance, often distributed across different teams and continents. Paysafe operates in a modern DevOps-oriented environment that encourages collaboration, autonomy in decision-making, and the use of modern infrastructure tools such as code, monitoring, and telemetry.

Employees have access to a variety of resources with up-to-date technical literature and certification opportunities provided by the company.

What role do you think automation and artificial intelligence will play in the development of talent in this field?

Automation and artificial intelligence are the main accelerators of technical development for candidates in the field of cloud technologies. They help create intelligent and predictable technical solutions, with an additional key feature: the ability to automatically recognize and correct errors.

By using automation for routine operations, specialists gain more time to invest in infrastructure development and decision-making for its improvement based on feedback from monitoring tools that track systems at multiple levels and build a comprehensive picture of behaviour.

What new roles or profiles do you expect to see emerge in the field of cloud infrastructure, and how should candidates prepare for them?

With the development of cloud solutions, artificial intelligence, and automation capabilities, traditionally known roles will evolve into new hybrid ones in line with market demand.

Some of these roles are:

Cloud-native Platform Engineer – focused on creating platforms for developing cloud-based business systems

Cloud FinOps Specialist – as infrastructure grows, so does the need for cost control and optimization

SecOps Engineer – working with infrastructure as code for security control, as well as building systems for early problem detection

To prepare, candidates need to keep up with trends in the field, as well as the increasing number of products appearing in the portfolios of companies offering cloud solutions. Researching and experimenting with new technologies gives candidates visibility into new opportunities that they can use as part of their work, apply them, and improve existing infrastructure solutions.

The material and image are provided by Paysafe

Why did you choose Java and its ecosystem?

I wasn’t particularly enthusiastic about Java at university, but my professional path led me to it in a very logical way. I started with data science and BI, then at Paysafe I mainly worked with Oracle and PL/SQL.

Gradually, while working on real projects, I began to get into Java through smaller tasks. I liked the structure, the capabilities of the language, and the strong ecosystem, especially in the context of large payment systems.

Over time, Java proved to be a natural extension of what I was doing. It provided stability, a rich ecosystem, strong tooling, and excellent integration with the architecture and SDKs we use at Paysafe. I moved from small tasks to more complex initiatives, and at one point I simply found myself in a role where Java was my main tool, and it was completely conscious and logical.

How has your work as a Java programmer changed in recent years?

My work as a Java engineer has changed significantly in recent years.

The language and ecosystem are evolving rapidly, which has reduced boilerplate code and increased productivity, making work more enjoyable.

Also, almost everything is now cloud-based. Technologies such as AWS bring enormous advantages, but this also means that programmers now need to be much more familiar with cloud architecture rather than focusing purely on their code.

The pace of work is also more dynamic. Releases are more frequent and require constant learning and adaptation.

The biggest change is the advent of AI, which I use as a tool for discussing ideas and speeding up routine tasks, but not for critical implementations. It is a valuable assistant, but it requires a careful and responsible approach.

Which new technologies or versions of Java do you think will have the greatest impact in 2025?

Over the past 10 years or so, the Java ecosystem has undergone continuous improvements. This applies to the language itself, as well as to the JVM and accompanying Java tools.

In my opinion, the technology that has had and will continue to have the greatest impact is undoubtedly Project Loom.

For me, this is a revolution in Java—virtual threads enable huge improvements in system performance and scalability without the need for particularly complex changes to the existing code or additional infrastructure resources.

Added to this are Structured Concurrency and Scoped Values, which further improve Java’s concurrency model and lead to more efficient management of parallel tasks.

At the same time, developments in the JVM and improvements in garbage collection make the ecosystem faster, lighter, and better optimized for cloud environments, a factor that is key for modern platforms and microservice architectures.

To what extent can Java adapt to cloud environments, microservices, and containerization (Docker, Kubernetes)?

I think Java is quite successful in adapting to industry requirements, mainly due to the development of the JVM itself, as well as frameworks such as Spring.

For example, effective management of hardware resources such as memory and CPU, due to the fact that the JVM is “resource-aware” to the container in which the application is deployed.

At the same time, modern Garbage Collection algorithms reduce overhead and make applications more stable and economical in cloud infrastructure.

The Spring Boot + Spring Cloud stack itself is the de facto standard in application development, specifically microservices in cloud environments.

It provides tools for service discovery, load balancing, centralized configuration, and much more.

This enables the Java ecosystem to create standalone, lightweight, fast-starting, fast, and easily scalable applications.

In my opinion, Java is not just adapting, but rather setting the standard in the industry.

What is the role of the open-source community and how does it support the development of Java?

The open-source community is perhaps one of the biggest forces behind the popularity and development of the ecosystem.

The OpenJDK project allows many companies and individual engineers to contribute improvements to the language, JVM, and tooling, which accelerates innovation and eliminates the risk of dependence on a single vendor. Without this model, the evolution of Java would be significantly slower and probably more expensive.

Open JDK allows everyone to have their own distribution, which they control without relying on a single vendor, and to use it according to their needs.

Many of today’s technologies in the ecosystem are due to it: Spring, Maven, Gradle, and many others arose precisely because the community had to solve a practical problem. Imagine dependency management without Maven/Gradle.

Every programmer can also challenge themselves and try to improve their skills by reviewing or even trying to contribute to an open source project.

I personally haven’t gotten there yet, but maybe in the future.

In short, I think that much of Java’s progress is due precisely to the active, strong, and innovative open-source culture behind it.

How do you keep your knowledge up to date in such a dynamic environment?

I try to keep my knowledge up to date by regularly reading articles, documentation, and technical blogs when time allows. Although I don’t always manage to pay attention to all the books and materials, I try to keep up with key developments in the ecosystem.

The team environment also plays a big role: conversations with colleagues, code reviews, and working on common tasks often reveal new approaches and technologies.

In my opinion, learning from other engineers is one of the most valuable things, regardless of position. The possibilities of the language are endless, and there is always something new to learn.

Here, I need to give a shoutout to one of my colleagues, Stefan Ivanov, who also works at Paysafe. He was the one who opened my eyes to virtual threads, which I had overlooked. Exactly one month later, I was already using them.

Do you think Java will remain one of the leading languages, and why?

I am convinced that Java will remain among the leading languages for many years to come. A huge part of global software—corporate systems, critical infrastructure, and government applications—is built on Java, and such a foundation cannot be replaced easily or quickly.

In practice, there is no other language that has proven itself and can consistently do a better job.

When we add the continuous innovations in the language and the JVM, as well as the strong open-source community, Java not only maintains its position but continues to evolve and become more accessible and attractive to new engineers.

The material and image are provided by Paysafe

Bike2Work is an ambitious initiative that inspires companies and professionals to choose cycling as a healthy, eco-friendly alternative to traditional commuting. Together, we’ll bring this vision to innovators, creators, and changemakers across industries through special initiatives, resources, and events designed to make cycling to work easier, more rewarding, and more impactful.

“This partnership aligns perfectly with our values of innovation, wellbeing, and positive social impact,”

says our CEO, Iva Abadjieva.

“Together, we can inspire change in both work and lifestyle.”

The initiative starts right here in Sofia, Bulgaria – home to the founding and core development team of WeRide.Today. Locally, we’ll be working through our Bulgarian edition, DevStyleR.BG, to engage and inspire Bulgaria’s vibrant innovation community.

Bike2Work has already made waves promoting greener commuting through tools, team challenges, community events, and real-time impact tracking. With our involvement, we’re ready to take this movement to new audiences – and invite you to be part of the change.

Image: Freepik

Microsoft has announced a series of significant updates to the Microsoft 365 Developer Program, aiming to provide developers with a more robust, secure, and accessible environment. These enhancements, shaped by community feedback, are set to roll out over the coming months.

Streamlined Tenant Provisioning

A new and improved tenant provisioning flow will become the default for all new qualified members of the Microsoft 365 Developer Program. These tenants are commercially enabled with add-on options. Existing members will have the option to transition by expiring their current tenant and provisioning a new one.​

Support for Commercial Add-ons

Later this year, members will be able to purchase additional subscriptions on their development tenants provisioned through the new experience, including Microsoft 365 Copilot licenses.​

Improved Tenant Ownership and Management

Microsoft is enabling clearer identification of tenant owners, making it easier for developers to manage and secure their environments.​

Option to Transition to Paid Subscriptions

Developers wishing to move beyond the Developer Program will have the ability to convert their development tenant into a standard paid Microsoft 365 subscription.​

Future Improvements on the Horizon

Microsoft is exploring additional ways to make the Microsoft 365 Developer Program more inclusive, flexible, and valuable for a broader global developer base. These improvements are currently in planning, with more details to be shared as the roadmap is finalized.​

What’s Next

A comprehensive update detailing the next wave of changes is expected by September 2025. Currently, no other changes are planned for existing Developer Program members beyond the enhancements mentioned.

Give the gift of self-dependence

This ability to have a bit more “life” alongside the work is really beneficial for your own well-being.

Daniel Pink covers how autonomy, mastery and purpose are the main drivers of motivation in his book “Drive”. Motivation, recognition and confidence are key to successful software development work. Being empowered to contribute toward a wider goal using your skills is way more rewarding.

According to research from Haystack, 83% of developers report burnout. That’s why the employers should be careful when they set realistic expectations for the software developers.

Education says you care

As an employer, you can invest in developers as individuals as well. Some companies offer generous training budgets or time off. If you don’t have the opportunity to provide a budget for study, you could still book one day a month to just learn something or ask for a one-hour tutorial from someone else to get you started on a new topic.

Freedom to work

Rewarding developers with money doesn’t work as a motivator. Giving them time and trusting them to use it for something other than direct product engineering work have a bigger  impact.

Google famously uses an approach of giving 20% of a worker’s time to be used for anything they found interesting. Atlassian is also famous for doing something similar, with all employees working for 24 hours on projects of their choice, producing surprising innovations and improvements that might never have shipped otherwise.

Lessons from open source

Whether remote by choice or circumstance, remote software teams today have much more impressive communication tools available.

However, all this connectivity can lead to added stress and notification fatigue. Software developers are all different. One person’s working style won’t be exactly like another’s. Open source projects work in a way that is respectful of everyone’s time and without much expectation that any one person will be around at any specific time.

Work-life balance

When the pandemic stopped us from our daily commute, many were left with less than ideal work setups.

Even if your developers have been working from home for some time, it’s important to check in whether they need a monitor upgrade, a spare power supply or even a new keyboard.

Take the time to socialize together at work. Some simple online games can lighten the mood. If your company offers an EAP (employee assistance program), make sure that all of your employees know about it and how to access it. It doesn’t hurt to remind managers that the programs are there for them, too, not just the people on their teams.

How and why did you decide to dive into the tech industry? What and/ or who has inspired you to do so?

It would be tough to point out one exact thing and say that’s exactly how it started. But if I had to do so, I would probably say it began by simple curiosity. And the way that usually goes is:

Hey, you know, what is this?

This is a computer.

Oh, how interesting!

And the… WOW, I can programme it to do what I want it to do and, and if it makes a mistake, it’s not because you know someone else’s fault, because I screwed something and that kind of combination of control and self-accountability but also the freedom. All those things that you can do with a computer can quickly get to be really addictive, so it’s really an addiction-type-of-thing. And yes, I guess that’s how it started.

Then I started doing this and it was all about getting this high and scratching this edge. It was more of a selfish endeavour in that sense when I really started loving what I do with technology but this was much later, I guess maybe five years ago. I was only doing it for myself but five years ago something happened. I ran across this interesting thing called Software-Defined Networking which was at the time, a really disruptive idea. Then I really understood that the power of these ideas can have to change everything, to challenge the status quo of the industry, I guess. And those pioneers at the time were really trying to do exactly that. Because networking is so very complicated and involves so many protocols. If you look at a diagram of protocols and want to print them out and put them on your wall, it will probably take you an entire wall. There are so many of them – hundreds of thousands and these guys were all about making things accessible for everyone else. Simplifying it so that anyone can get involved, innovate, contribute and attend. And I guess that’s when I really started gaining some perspective on things and I’ve been doing that ever since.

We’re not doing technology just for the sake of doing technology. Actually, I was kind of doing that before. Then I realised that technology is just a tool, something that everyone can use and needs to access.

What have you been working on for the past few years?

I think the last few years have been the most interesting years in my career, but then I could have said the same for every year, whenever you asked me that.

So before I get into details I think I need to come clean with something. In my bio and on my LinkedIn account I say that I work for SUSE. That is a lie. Let me explain. You know they say “Choose a job and do something that you love so you never have to work a day in your life.” And that’s the case with me. That’s how it feels working with an open-source company like SUSE. They say that open source is in our genes there and that’s true.

So, I started working maybe five, or more than five years ago as an OpenStack cloud software developer. OpenStack is a complex cloud platform software. It was also a disruptive technology cloud for a while, hence, I started to do that. Then, a few years after I made the transition to something else equally interesting to containerization, Kubernetes and containers. I was also a cloud-native application developer for a while.

But I guess the really interesting story of mine is when I started working in doing things with artificial intelligence and machine learning. In 2020, late 2020, I got involved with this amazing group of people, and we created this open-source project called FuseML. This is partially the reason why I gave this talk to the ОpenFest. And we recently launched it. We’re trying to improve things for everyone that wants to do something with machine learning.

You have a lot of experience in the tech world, starting from virtualization to SDN, to cloud computing, to containerization and cloud-native applications. Now you are exploring the AI/ML realm. Can you share what are the main challenges that stand in front of the AI/ML world?

Sure, so it depends on who you ask. For companies, I would say the challenge is to not really read the benefits of machine learning.

So far, only the big giants and the companies have been able to successfully do that and that’s why you hear all these interesting stories about artificial intelligence and machine learning coming from companies like Google, Netflix, Uber and so on. The real challenge here is to democratise that type of success and to give everyone a fair chance. I think that’s what these new disciplines of ML ops, machine learning operations are trying to do together with a set of best practices to help with that. So, it aims to help facilitate everyone’s access to building machine learning systems that everyone can put in production and get the revenue out of that.

For us, end-users, well, not for me because I’m a developer but I guess I’m also an end-user of machine learning… So, for us and users and maybe even the society at large the challenge is huge because it’s the world we need to understand. I don’t think there are a lot of people that really understand what machine learning is all about and what artificial intelligence really does. So I think that would be the next challenge for us, to inform ourselves and for those who know about it, to popularise it and democratise it in a way that makes it accessible to anyone. In that way, we’ll make it easy for everyone to understand what the implications of machine learning are and how we affect them personally because they will affect us as well.

And I think that that is one way to do it. So, again, I keep going with the democratisation of machine learning. I think that is one of the best ways to tackle this problem and this challenge. Also the popularisation. I hope there’ll come a time when we need to standardise what we’re doing with machine learning in the industry.

What specialized interpretation of the traditional DevOps culture and methodologies are required to build and maintain a successful production Machine Learning system? 

It’s not so much in my opinion as it is something that is still developing. It’s something called ML ops and machine learning operations. So people have tried to apply DevOps, standard DevOps, conventional DevOps to build machine learning systems and machine learning applications. And it didn’t really give us a day or hope for – I’m talking mainly about companies that are trying to put production machine learning systems out there, and the reason for that is the fact that machine learning systems are unique. You need to change the way you think about machine learning because they are unlike anything we’re doing within conventional software engineering.

So, DevOps for machine learning requires a more targeted approach to apply them to machine learning and that’s what ML Ops is. It basically looks at machine learning and then says and recognises that machine learning is weird. So, what I’m thinking is that machine learning is the quantum mechanics of software engineering. Quantum mechanics is really weird when compared to classical mechanics or Newtonian mechanics. They can behave in all sorts of unexpected ways, they are really opaque. You cannot directly measure it, you cannot actually measure what it’s doing as you do in classical mechanics.

So you don’t really know what’s happening inside. You need to take a lot of measurements, you need to do a lot of experimentation with machine learning models to understand how they behave, and to change them to behave the way that you want them to. So it’s really, really unlike anything that we do with conventional engineering.

What AI and machine learning tools are you familiar with?

My experience with machine learning tools and additional registers comes mostly from what I do with this fusion project.

And, gosh, I wish I had more than 24 hours in a day because there’s like an entire ecosystem of things, tools and ideas that are constantly expanding and evolving. Some more research papers are being published each and every day in the machine learning field. More tools are being able to capture those ideas and I can only scratch the surface with what I’m doing.

But in my experience as an engineer that is working on something like ML ops, that is maybe closer to the tools that you need to successfully put machine learning in production. So I can give some examples there. I have some experience with tools that relate to those that you use to track what you do with machine learning models, to Version Control Data and all these artefacts that are coming from machine learning development. Things like ML flow, that is a very popular tool for doing that. And not just that but also it’s really popular for data science DVC, and that stands for data version control, which is another tool that I briefly worked with.

Well, the tools that are inside a component that can be used to generate predictions are called Prediction Service platforms. And today I’m also dealing with my voice. And, I mean I can go on and on.

You need these pipeline orchestration engines to implement DevOps like workflows and that are specific to machine learning things are tacked on Argo and kept for pipelines. And yeah, I think that there are maybe hundreds of thousands of doors in here. It’s very hard to keep track of all of them.

You have mentioned that you have dealt with a lot of machine learning problems. What kinds of machine learning problems have you tackled, and how did you tackle them?

Yeah, so those again are related to what I’m doing. It’s an orchestration tool for ML ops and because of that machine learning sometimes involves a lot of tools. So sometimes you need several of those tools to glue them together.

Because every tool serves a localised purpose in what you’re doing to create machine learning systems that are production-grade, one challenge that we’ve been trying to do with FuseML is how to integrate all those tools together, how to get open source tools from various open-source projects, unrelated to one another, and create a complete end to end workflow on top of them. How to integrate them in a way that, first of all, they work together, because sometimes they don’t. And how we did that is, we’re featuring all these Extensibility Mechanisms. We fuse them out with a FuseML project. We can integrate all these tools, and use them as components in your automated workflow with minimal friction.

I guess the way that an end-user would see that someone that uses FuseML is through abstractions. So through abstractions, it is a very nice way of bringing this thought again. Extracting simplicity out of complexity and giving everybody a chance to interact with those that can be complicated and really not challenging to interact with otherwise. So that’s one problem that I tackled. And that’s what, not me myself but the whole team from the project does.

Peace of mind is also an automation tool that automates the workflow of machine learning and building machine learning systems. The question was how. So, how do you deal with it? How can you find a balance between automation on one hand and customization?

So how to have a tool that allows you to automate everything that you want to automate but at the same time gives you the control you need to customise the way it’s running all those processes that are needed to implement the workflow that you’re trying to automate, how do we tackle that one?

Well first of all we recognise that there’s a problem that needs to be solved. So, let’s say you have some images, and you want to recognise you do not want to do object detection from those images. You can have an end to end, fully automatic workflow that does that for you – you just deploy it, apply it to those images and it does that for you with us. But you also can get very personal and particular about how you’re doing things. You can split that workflow into PCs and orchestrate them in a more customizable way.

What are the ethical implications of using machine learning?

Yeah, that’s what everybody is thinking about. I guess that doesn’t matter what we are doing with machine learning. I don’t know if I’m prepared enough to answer that question myself. Everybody has their own interpretation of things. It is a big challenge that machine learning in the industrial sector still needs to deal with that and even more. I myself have to admit I’m still struggling to understand what those indications are but probably time will show that. So I think machine learning has the capacity to transform society, on a very deep level. I guess everyone is concerned with things like privacy, surveillance, and, of course, bias and discrimination because machine learning systems can do that if they’re not properly built.

And these are maybe personal things. So how does it affect me and how will it affect me? How will machine learning systems take away my job? Will it, and how will it impact me? Will this benefit me as a person, as an individual?

I think the more pressing question and the more pressing ethical implication is how it will impact us as a society. There’s one example that I figure we can just give a thought of how much we need to how much we lie on a day to day basis to friends, to relatives, to our children, to our employer, employees. So we do that, maybe even to ourselves sometimes because we need some kind of reassurance on different things.

So as a society and as individuals, we really rely on a lot of stuff that is distorting the truth. So imagine that there will be an app out there, some point in the future and it won’t take too long to come up with that kind of thing that can detect whether you’re telling the truth. You can install it on your phone, you can screen your conversations like the conversation that we’re having right now. So, everyone can have such an app on there, or on their laptop installed and they can track the eye movements, the way our lips move the inflexions in our voice. It can tell us whether we’re telling the truth, or whether we’re not really truthful about what we’re saying.

Now imagine the implications of that and how we can deal with that as a society, as individuals. I think that’s the kind of thing that keeps me up at night. And I really wonder whether we as a society, as a human species, were able to cope with those types of changes. Because everyone has their personal interpretation of those occasions.

So let’s move slightly to the OpenFest 2021. At this year’s OpenFest2021 you presented “MLOps: specialized DevOps for Machine Learning”. What is the reason to choose this topic? How important is it?

Yeah, so it all goes back to what I was saying earlier about FuseML. This project that we’ve been working on at SUSE, with the ML team there. So ML is an emerging engineering discipline where there is still a lot of experimentation, a lot of thoughts being gathered, a lot of ideas being exchanged as practices. They are being collected and I think people need to be aware of what’s happening. Because it’s only through collaboration and exchange of healthy ideas. There’s even an ML ops community, where people that are interested in that exchange ideas. And that’s some of the reasons why I think everyone needs to know about ML ops.

What is your message to all beginners and tech newbies? 

Well, if you want to be successful in this industry, that’s really highly competitive, find something that is complicated to use, simplify it in that way so it’s not widely available. Make it accessible to everyone. And I think in that way you’ll not only have to benefit yourself as someone who is passionate about technology but also contribute to the larger picture.

According to the report, continuous testing and continuous integration are both markers of success for elite performers. Another key to their success is trunk-based development.

Trunk-based development is a source-control branching model where developers collaborate on code in a single branch, known as the trunk, and restrict their work to within that trunk. The purpose is to streamline merging and integration phases. This development process increases the optimization of continuous integration and continuous delivery (CI/CD) services, which in turn increases software delivery efficiency.

The study also found that elite performers were 3.4 times more likely to execute database change management. That adds database maintenance to the list of critical success factors. Observability was another metric that defined an elite performer. Organizations that leverage observability tools such as AIops are 4.1 times more likely to have solutions that incorporate observability concepts and technology.

What does this all mean?

Here’s the first and most obvious observation: Organizations that use hybrid or multi-cloud are more likely to deploy DevOps best practices and toolchains. In this scenario, the appetite for risk is higher, as are the budgets to experiment with emerging technology such as cloud-based DevOps tools, databases, and observability. The ability to leverage new technology relates directly to the amount of risk and cost an organization is willing to invest in its future.

Putting that aside, for now, this report makes clear that most IT leaders with heterogeneous cloud deployments, including multi-cloud and hybrid cloud, have more choices of platforms for deployment and more choices of available tools. These leaders have access to better DevOps weaponry, which draws a straight line between the ability to pick best-of-breed technology and elite performer status.

Another bonus?

Because they can choose options from a larger pool of technology, the end-state solutions are more likely to be optimized as to capabilities and cost. When comparing the abilities of elite performers to the rest of the pack, here’s the best analogy we can think of: The elite performers shop in a large department store that has all the best brands of coffee makers at all the best price points. Everyone else shops at a small-town hardware store that only offers a few brands with a limited price selection.

This does not mean that those who leverage multi-cloud and hybrid cloud will be elite performers by default. It does mean that those who open their cloud platforms beyond a walled garden are more likely to have optimized solutions for development, deployment, and operations.

Both career fields require extensive programming experience, a strong attention to detail, and a knack for problem solving, but they have different workflows, maintenance requirements, and levels of complexity. For example, web developers often work in close collaboration with business and marketing experts to ensure their company’s website is efficient, user friendly, and aesthetically pleasing. Software developers, on the other hand, tend to work with other tech-oriented co-workers on large-scale software, operating system, and mobile application projects that might not require input from other business units. Ultimately, the career path students choose should align with their professional goals and personal interests, which is why it’s crucial to explore each role in detail before committing the time and resources.

Becoming a Web Developer

Web developers are responsible for how a website or web application looks and functions, from its user interface and page layout to back-end systems for gathering data. They work with organization leaders to design unique and engaging websites for businesses, nonprofits, government agencies, and anyone else looking to communicate more effectively online. Web developers are also in charge of maintaining the performance of the websites and applications they create, ensuring users have a consistent and positive browsing experience. According to the U.S. Bureau of Labor Statistics, common web developer job responsibilities include:

• Coordinating with clients/business leaders to outline new web design projects
• Creating and testing web applications and website features
• Writing code in various programming languages, including HTML/CSS, XML, and JavaScript
• Integrating content into websites, such as graphics, written copy, videos, and audio
• Establishing technical requirements to support websites’ long-term functionality
• Developing security elements to protect user data and business assets
• Monitoring website traffic and performance

Diving a bit deeper, the web development field can be broken down into three specialized areas based on a candidate’s particular skill sets and professional interests: back-end developers, front-end developers, and full-stack developers. Each of these web design roles comes with different expectations and responsibilities:

Back-end web developers: These professionals focus on the technical aspects of websites and web applications that support both basic and advanced functionality. They largely work on administrative components including databases, website architecture, and application logic, creating new APIs and user interfaces based on predefined specifications. Generally speaking, this type of developer is concerned with how websites and web-based applications work on a fundamental level.

Front-end web developers: Sometimes called client-side developers, these design experts are in charge of how websites and applications look and function from the users’ perspective. They are responsible for making all online content easy to access, browse, and interact with, especially for users who may have limited computer skills. Front-end web developers must also ensure their websites are compatible with a wide range of operating systems, browsers, and devices to prevent display and functionality errors.

Full-stack web developers: As the job title suggests, these web design professionals are proficient in both front- and back-end development tasks. Most full-stack web developers have extensive experience in application design, user experience, and programming languages like HTML, XML, JavaScript, MySQL, and others. As such, they are able to fill advisory roles and technical positions that require both computer science and business savvy.

Becoming a Software Developer

Software developers invent, manage, and optimize computer programs that run on desktops, laptops, smartphones, and other mobile devices. Using different programming languages ― such as Java, Python, C#, and SQL ― these computer science professionals write complex code that governs how desktop applications function. Many software developers work for large technology companies, like Microsoft and Oracle, designing new products or fine-tuning existing applications. This includes troubleshooting code bugs, updating user interfaces, creating new in-app tools, and much more. According to the BLS, software developers have some combination of the following responsibilities:

• Analyzing users’ needs and designing software-based solutions
• Recommending software upgrades to existing computer programs and systems
• Designing new applications for specific audiences (consumers, enterprises, etc.)
• Creating detailed models and diagrams that outline which software code is needed
• Documenting all aspects of application and system design for future reference
• Testing code for new applications to ensure consistency and efficiency
• Ensuring software is compatible with present data management systems

Software developers are heavily involved in every stage of the application design process, from the initial planning to the final rollout of new computer programs. However, just like web developers, professionals in this field are often categorized into specific roles based on their specializations: software applications developers and software systems developers.

Software applications developers: These professionals tend to focus on designing specific desktop and mobile applications, tools, and games for consumer audiences, according to the National Center for O*NET Development (NCOD). They often work on the same project for many years and are responsible for ensuring their software is functional, engaging, and efficient.

Software systems developers: Developers in this role are largely concerned with designing systems-level software for enterprise customers, rather than the public, according to the NCOD. They use their extensive programming knowledge to create computing applications for a range of industries, from manufacturing to aerospace and beyond. The software they develop is used to manage corporate networking apps, database management systems, and other critical infrastructure.

For some developers, these platforms are a welcome time-saving solution to focus their efforts on other higher-value projects. While other developers might disagree and instead of fear that low-code and no-code platforms will result in fewer jobs for developers. A survey uncovered the good–and the bad–of low-code and no-code platforms and what their usage means for the enterprise.

The survey asked the following questions:

• Does your organization currently use low-code or no-code platforms?
• Does your organization plan to use low-code or no-code platforms in the next 12 months?
• How is your company currently using or planning to use low-code or no-code platforms in the next 12 months?
• Which of the following benefits does your company get or expect to get from low-code or no-code platforms?
• Which low-code or no-code platform is your company currently using or planning to use?
• Why isn’t your organization currently using or planning to use low-code or no-code platforms?
• Do you think low-code or no-code platforms will result in fewer jobs for developers overall (not just in your organization) in the future?
• Why do you feel that low-code or no-code platforms will result in fewer jobs for developers?
• Why do you feel that low-code or no-code platforms will not result in fewer jobs for developers?

Low-code and no-code (LCNC) software development platforms are steadily gaining in popularity as businesses seek to streamline workflows and digitize business processes. Nearly half (47%) of those surveyed currently use LCNC in their organizations. Of the 35% who are not currently using LCNC, one in five (20%) said they intend to adopt the technology in the next 12 months.

Most respondents are using LCNC to automate workflows (17%), create new applications (15%), speed up development time (15%) and automate data collection and reporting (14%). Another 10% of respondents mentioned reducing the burden on developers and connecting and creating inter-departmental applications, workflows and business processes for reasons to incorporate LCNC.

The survey found that the ability to utilize LCNC to provide business solutions provides many benefits to organizations. The top benefit survey respondents receive or expect to receive from LCNC platforms is improved productivity (15%), followed by reduced application development time (14%), and automating manual processes (12%). Rounding out the ranks of top benefits was increased use of automation in business processes (11%), and at 10%, streamlined, easier-to-use workflows, empowering users to solve problems, and reducing dependence on spreadsheets.

The majority of survey respondents (67%) do not think low-code or no-code platforms will result in fewer developer jobs. However, 16% of respondents do. They cited that developers are too slow to help businesses respond to fast-changing market conditions and opportunities and that developers will feel undervalued and will quit instead of working on these platforms as the top reasons why.

Focus on progress

When it comes to web development, it’s an always-changing programming niche. If you aren’t up-to-date with some trendy technologies, languages, or just with fundamentals, you won’t be ahead of others, and that can lead to you not landing that job.

Plan it. Take help from the roadmaps, like a “Front-End Developer Roadmap” created and available on roadmap.sh.

Try learning techniques

People use different learning techniques, but all of them are similar to one, which you probably know already — the Pomodoro technique. That’s the most effective learning technique for most programmers.

Teaching mindset

When you become a teacher, you’ll not only help others, but you’ll also help yourself! Sharing knowledge is always helpful.

Note your progress

If you write something down on a paper, you’re thinking of what you’re currently writing, you are reading it, and the most important — you understand it better.

Avoid perfection

You will never be able to do something flawlessly. No one will. When it comes to programming, there will always be a better approach, a better understanding, or whatsoever. Rather than trying to do things superbly, make things work, and do it briefly.

This type of approach can slow down your learning process and keep you away from progressing.

Remember to rest

Keep in mind that resting is as important as learning. Especially when it comes to programming, where all you do is learn — you have to take breaks. It won’t only make you feel better, but it’ll make you ready for the new challenges that you’ll face every day as a programmer.