Will ChatGPT Replace Software Developers?

Will ChatGPT Replace Software Developers?

Artificial intelligence (AI) has the potential to transform the world as we know it. As data becomes increasingly digitised, AI will be used more and more for decision-making and optimisation and will replace more manual and labour-intensive jobs, tasks and processes. The technology has the potential to take over the manufacturing, delivery, design and marketing of the vast majority of products and services. The question is, will it leave humans obsolete in the process? And what about software development itself? 

What is ChatGPT?

ChatGPT is an AI-based chatbot created by the OpenAI platform. The concept is similar to a normal search engine. However, instead of giving many different search results, it gives one conversational result. ChatGPT is a hot topic at the moment, with over a million users signing up for the research preview at the beginning of 2023. The groundbreaking language model from OpenAI is a versatile tool that can be used in a myriad of ways for everything from translation and language modelling to content creation and, you guessed it, coding. Whether you need a quick answer to a question or complex explanations, it has the power to deliver. 

However, while its conversational capabilities as a go-to source for information seem obvious, the question on everyone’s lips is how far can the technology go? Will ChatGPT replace Google as the top search engine? And will it be able to take over roles such as copywriting, code testing and, indeed, software development itself? 

Could ChatGPT Replace Software Developers?

Before deliberating whether ChatGPT will replace the role of software developers, it’s worth looking at whether it could. As it stands, the AI-powered chatbot can simulate human conversation and write simple web pages and applications, find bugs in code and even create new programming languages. However, writing code isn’t the same as replacing the whole software development process. The technology can’t, at this time, create new code by itself. Instead, it uses existing code available from open-source repositories and provides the best result for any given problem. 

So, while ChatGPT is more powerful than any other search engine to date, the technology can’t completely replace the need for the human mind. For ChatGPT to replace software developers, it would need access to more than the internet; a company would have to give it access to internal data and it would need to be significantly modified. However, to implement and modify ChatGPT as it stands to fit any particular company structure, you’re looking at a huge amount of resources, which just can’t be justified. While there is no saying exactly how far the technology will go, in the short term, there is far more to be gained than lost from  ChatGPT.

The Benefits of ChatGPT for Software Developers

While many people worry about the potential for AI to take over their jobs, software developers tend to look at AI for what it can bring to their profession rather than what it will take away. Advancements in automation enable them to be more efficient and write better code. What’s more, tools like ChatGPT can remove a lot of the pain of learning how to code in the first place, enabling more people to enter the industry and helping to solve the ongoing talent crisis in technology and engineering. ChatGPT removes barriers to entry for novice coders, helps them keep pace with more experienced coders, and provides answers to specific problems. 

Developers, engineers and programmers don’t work in a vacuum; they collaborate and share in open-source communities. By using ChatGPT, they can collaborate together to solve problems and generate the code they need on the spot rather than unnecessarily reinventing the wheel. Meanwhile, software developers have the potential to truly invent or create something new or unique. Automating mundane tasks frees developers up for a higher level of thinking and gives them an opportunity to be more creative and stand apart from the competition. Instead of having to write boilerplate code, they can focus on complex application architecture. After all, software development is about so much more than coding alone; it’s a unique skill to structure programmes, follow logic and generate something greater than the sum of its parts. 

In addition, ChatGPT makes AI more accessible to businesses that aren’t traditionally tech-savvy and, as adoption goes up, more developers will be needed who can implement and understand those systems and maintain the technology. In this way, the role of a developer might evolve. As ChatGPT and similar tools get smarter and better at writing code, fewer engineers will be needed for certain tasks. But there will be a greater need for developers who can monitor, maintain and assemble that code into functional products. Instead of developers writing themselves out of a job by kickstarting the wave of AI, they’ve made sure their skills are more in demand than ever. And new roles will emerge, such as prompt engineering; the need to write model inputs to get the best possible results from chatbots. The skill of a software engineer will be more in demand than ever. 

The Future of AI in Software Development

Without modifications, at the time of writing, ChatGPT is really just a co-pilot for this type of work. However, there is no way to know what the next versions will be like. No doubt they will be more powerful with huge potential. While the technology can’t write complex code just yet, it is certain to become more proficient. Regardless, Instead of ChatGPT and other AI technology taking over the role of software developers, it is an opportunity for them to work faster and smarter. 

There will also be a place for human software developers to shine. While AI will be able to deliver more and more, the human mind will always be needed to pull everything together in a way that makes sense. AI will clearly hold a very big role in the future of software development; however, it’s a very long way from being able to add a human level of creativity, emotion and understanding needed to take over from developers entirely. The fact is that it takes humans to build experiences for humans. 

Future Trends in Software Development

What the global pandemic, global recession and supply chain issues have taught every business is that it’s fundamental to build systems and processes that are agile and can adapt to new requirements. Ultimately, we need to be able to prepare for the unexpected and software development forms a fundamental part of that. 

Technological demands will continue to rise as businesses, large and small, aim to streamline processes, improve efficiencies and deliver more value and better customer experiences. And as powerful technologies such as cloud services, artificial intelligence and blockchain come to the forefront, organisations are expecting more from their software solutions than ever. 

Trends That Are Becoming Widely Adopted

With the rapid digital transformation and move to remote working that has happened over the last couple of years, some upcoming trends have quickly moved to more mainstream adoption. Organisations need to be aware of these trends and consider adopting them to remain competitive. Here are just some of the trends that have become more widely adopted:

1. Low-Code/No-Code Platforms

By using low-code or no-code platforms, developers and non-developers alike can build platforms without using any coding. This gives smaller organisations or those on tight budgets the ability to create their own functional applications in-house. Meanwhile, those who use a development team can achieve more. Professional developers can work more efficiently and, with the basics covered, can focus on business logic and custom integrations. No-code tools are set to increase in popularity further, putting power in the hands of every business to achieve more. 

2. Automation

Automation is becoming key to every business; it enables manual processes to be eliminated along with human error, delays and frustrations. And automation is used within software development itself, with AI now beginning to write code. There are very few areas that automation doesn’t prove to be a valuable development tool; from coding to quality assurance, it can speed up the development process and greatly improve productivity.  

3. Serverless Computing

Traditional software developers would need to facilitate the process of deploying their code to the server or the cloud; however, serverless computing works to automate that process. This helps businesses to leverage the benefits of automatic scaling within their cloud infrastructure. With serverless computing, developers can build and run application code without worrying about provisioning or managing servers or backend infrastructure. As with low-code platforms and automation, this saves valuable time and enables developers to focus on custom features. 

4. Big Data

Businesses collect a massive volume of data but are often unable to process it in real time. Instead it is catalogued in large and, often, unstructured datasets. A data scientist can examine and organise sensitive and valuable data and organise it in a structured, cohesive way that can be consumed by other systems and used to improve processes, gain invaluable insights and drive business growth. 

Cutting Edge Trends to Keep an Eye On

The cutting-edge is where the most innovative and pioneering technology can be found and it’s where the real action is happening where software development is concerned. While some of these technologies may already be on your radar, they’re set to evolve and expand exponentially over the coming years:

1. Artificial Intelligence (AI)

While we may no longer see AI as something new, it is rapidly evolving and is being applied further to areas such as cybersecurity. The technology can help automatically detect malicious behaviour within software and identify breaches. In addition, AI is being developed further for use within chatbots and conversational interfaces. ChatGBT (Generative Pre-Trained Transformer), launched by OpenAI in November, is quickly gaining in popularity. It uses adaptive human-like text to answer questions, write stories and engage in dialogue. In fact, it can also debug computer code, admit mistakes and reflect incorrect requests. It’s a clear example of how advanced AI is becoming and could well replace traditional search engines like Google at some point in the future.

2. Machine Learning

Machine learning is a part of artificial intelligence that makes decisions based on previous information or real-time information. Again, as with AI, there are already many applications of machine learning. Voice assistant technologies like Alexa are examples of how heavily technology giants have already invested. As machine learning learns, making decisions based on previous information, it stands to reason that the more it is used, the better and faster it will become. Machine learning is set to be a fundamental component of future software applications. 

3. Blockchain

Blockchain technology creates a secure, decentralised database record of all transactions in a sequential order and, importantly, that record can’t be altered. Blockchain adds security and transparency to transactions and is becoming more widely used. While predominantly thought of in relation to cryptocurrencies it also offers the ideal solution for creating trustless systems, storing data in transparent ledgers and moving data in peer-to-peer networks. As mobile applications need to become evermore secure, blockchain is becoming increasingly popular. 

4. Deep Learning/Neural Networks

Deep learning is a part of machine learning involving a neural network with three or more layers. The neural networks aim to simulate the behaviour of the human brain and learn from large sets of data. A network with a single layer can make approximate predictions but the additional layers in deep learning work to optimise and refine accuracy. Deep learning will be used more frequently to enhance coding design and software delivery and alleviate some of the extensive organisation and preparation work from development teams. 

5. Business Process Orchestration

Business process orchestration is the coordination, synchronisation and monitoring of multiple automated workflows. While it involves automation, it takes the concept to the next level, ensuring workflows work together in a cohesive way that fosters collaboration and communication. Business process orchestration delivers a holistic view of processes and workflows and integrates all the software and applications used across different teams into a single platform with a single view and a single source of truth. 

Predictions for the Future of Software Development

Software development is a rapidly evolving industry and is invaluable for adapting processes and services to meet changing market needs and user expectations. We can’t know for sure how new technologies will evolve, but they are certain to focus on the key areas of agility, quality and security. 

What is certain is that software development will continue to be key to our businesses, their operation and, ultimately, their success. Having a handle on the future trends that might shape software development offers invaluable insight into future opportunities that will help businesses to stay ahead in an ever-competitive environment. 

6 Ways Machine Learning is Improving Software Development

Machine learning (ML) is a subset of artificial intelligence that uses algorithms to enable computers to learn different tasks without being explicitly programmed; the more training data a computer has access to, the more accurate it becomes. The technology enables computers to solve problems that aren’t easily solved using traditional methods and is commonly used for applications such as speech recognition, image recognition, natural language processing and autonomous driving. 

Of course, the current applications of machine learning are going to evolve as we move forward and that is going to be felt across every industry. Machine learning is beginning to change how traditional software development works at its core, which will have ripple effects to many applications. The technology enables systems to function independently and, as such, developers have more time to focus on innovation. As such, machine learning is making it possible for developers to create smarter, more efficient applications. Here are just six ways that machine learning is improving the world of software development:

1. Handling Code Reviews

Software has to meet a lot of functional and non-functional requirements and code needs to be clean and bug-free. Coding guidelines are there to ensure there are no unnecessary complexities, security concerns or performance issues. However, while following coding guidelines is necessary to ensure work standardisation, it’s no mean feat. To ensure that any development team follows coding standards, code review is needed, which can be incredibly expensive, not to mention time-consuming. 

Machine learning helps handle code reviews by providing tools that can automatically search for common deviations from coding standards. Using ML-powered tools to assist code reviews can reduce costs, improve efficiency and, most importantly, ensure there are no application security risks in the software that is being developed. Machine learning can help to review and restructure code, making it not only in line with coding guidelines but more readable, consistent and performant. The same ML-powered tools can be used for maintenance, helping to clean, debug and modify code quickly and shortening the entire process considerably. 

2. Delivering New Insights

Machine learning is going to become increasingly vital to all businesses to deliver insights and that is especially beneficial in complex environments such as software development and IT teams. When there are conflicting priorities, multiple solutions from different vendors, several frameworks and tools and many stakeholders, IT environments quickly become complicated. Not only does the environment need to be simplified, but insights are needed to help manage conflicting priorities. 

Machine learning tools can study code repositories and gain actionable insights around things such as legacy code, whether there is any code that isn’t maintained, how many apps aren’t adapted to the cloud, what is slowing down a development team and more. With these ML-based tools, teams can become more aligned, collaborate better, focus on reusing code, make processes more efficient and deliver better solutions as a result. 

3. Managing Project Forecasting

Software development projects can be incredibly complex, with schedules and costs to manage as well as quality and risk management. With the help of machine learning, project managers can forecast when projects will be completed with accuracy, with algorithms taking into account team composition, past performance and the speed at which a team is able to complete tasks. The result is that project completion dates aren’t based on best guesses; they’re based on science. 

With ML-powered tools, development companies can use external data and information repositories to identify risks, review project documentation and more. What’s more, with M-powered dashboards, all of the information is easily available, improving project accuracy and cost estimations and reducing time to market. 

4. Automating Testing

When it comes to software development, there are lots of manual, repetitive tasks which need to be managed. However, while rule-based automation is already used for some tasks, machine learning is creating the possibility to automate a whole new array of tasks. New ML-based code review tools can learn from source code repositories to find critical bugs, working with multiple programming languages and learning through thousands of high-quality open source repositories. The tools can analyse the intent of code and highlight critical security vulnerabilities. 

ML isn’t used for automation as much as you might expect at the moment but it’s expected to grow exponentially. Smart programming assistants can read technical documentation and debug code by sifting through massive volumes of data and self-correct anomalies. The technology can also help create tests themselves, which has the potential to vastly reduce development time. 

5. Improving Data Management

Machine learning is the perfect solution when it comes to big data as algorithms can learn from a wide range of patterns and trends. With this ability, response times are reduced considerably. Machine learning can integrate data from multiple sources in a much simpler way than traditional data indexing and without the huge memory consumption required. This means that developers can manage data pipelines much more effectively, 

6. Writing Code

Not only can ML be used to test, monitor and update code, by using the right combination of deep learning and code structure recognition, it can actually be used to write the code itself. While developers need to create the source code, ML can create subsets to accompany that code, fill gaps through self-learning and convert diagrams into code. And, in the process, ML models can assess risk, detect anomalies and ensure security processes are in place. With the use of machine learning, thousands of lines of code can be reduced into hundreds, saving a huge amount of time and resources and enabling developers to focus on more value-based work enhancing the code. 

Will Machine Learning Improve Your Next Project?

Machine learning is rapidly changing the field of software development, making it more secure, efficient and reliable than ever. Of course, building machine learning solutions and tools requires extremely niche skills and the technology isn’t something everyone has access to as yet. What’s more, while programmes can learn and improve, they don’t have the ability to understand emotions and truly mimic human brain activity in the form of an artificial neural network; you still need humans on your side who know what they’re doing. 

If you’re working with an experienced software development company, there is a good chance that they’ll already have incorporated machine learning tools into their development process, meaning that you can create more efficient, powerful and innovative applications for your business

How Digital Transformation Can Drive Sustainability

If your business isn’t considering sustainability, it’s well overdue. Governments, employees and consumers are all pushing for businesses to prioritise environmental concerns and many consumers are willing to pay a premium for sustainable brands. However, it can seem a big ask, especially when there are many other priorities fighting for attention. 

Digital transformation and sustainability aren’t always thought of as working together and can compete for attention in the boardroom. However, when digital solutions are built and implemented with sustainability in mind, they can become complementary drivers. By integrating sustainability into transformation efforts, it’s possible to both create more sustainable solutions and drive revenue growth at the same time. Efforts to create more sustainable processes are no longer just nice to have, they’re fundamental for the future. When done in the right way, digital transformation can deliver the balance between being more sustainable and optimising business operations to improve the bottom line. 

The Conflict Between Transformation and Sustainability

With the aim to secure global net zero emissions by the middle of the century, sustainability has never received more attention. World leaders have called on industry to limit emissions and act in a more sustainable way. Digital technologies are often hailed as enabling tools for sustainability, they can save energy, connect people globally and inform decisions through big data. However, digital technology most certainly isn’t carbon neutral; in fact, it accounts for a huge amount of global emissions. While it can improve productivity and drive business operations, the rapid growth of the digital universe correlates to huge energy consumption and physical e-waste that contributes to climate change.

What is Sustainable Digital Transformation?

Sustainable digital transformation means doing the right thing when building and implementing digital solutions and products. That means considering the impact of both what is built and how it’s built. To be responsible, businesses need to not just implement digital solutions, but use tools that will track the impacts of those solutions. It’s a case of asking sustainability-driven questions throughout the whole development process and product lifecycle to ensure that systems and applications do more good than harm and new issues aren’t introduced. Of course, while sustainability is extremely important, it’s also vital to ensure digital transformation delivers a return on involvement. 

Sustainable digital transformation means embracing technology and using it in the right way so that digitisation and sustainability work together. When done right, digitalisation enables businesses to embed sustainability into their systems, optimising production outcomes to create the greenest output while also reducing costs. For example, data can be captured and operational processes run in the cloud to enable remote collaboration. It’s an opportunity to transform, embed data intelligence and predictive analysis throughout the supply chain and bring about a new decade of sustainability. 

Sustainable innovation often results in solutions that are less expensive, have less environmental impact and improve employee retention. After all, digital transformation projects can provide real-time information and measurement of processes with great transparency, and use it to reduce waste. With a commitment to sustainability, digital transformation can drive inspiration and innovation. 

6 Ways to Achieve Sustainable Digital Transformation

Sustainable digital transformation is a long-term commitment that involves a complete change in mindset. While every business wants to be successful, putting sustainability as a top priority means considering the wider implications of every decision first. There are several ways that businesses can make their digital transformation efforts more sustainable: 

  1. Build Data Integrity – it’s impossible to manage something if it can’t be measured. Measuring the success of digital transformation and sustainability efforts and keeping stakeholders, decision makers and end users informed ensures expectations and perceptions are managed. Building data integrity enables you to visualise what good looks like, understand the impact of your actions and trust data to make sustainable decisions.
  2. Align Infrastructure – many businesses have fragmented systems with a complex mix of legacy and enterprise architectures. To be able to capture the value of data and drive more sustainable solutions, there’s a need for a unified approach to digital transformation where systems are integrated, aligned and able to deliver more efficiency in terms of energy and cost. 
  3. Be Flexible – creating linear systems is no longer good enough. The issue is that, while they work in the first instance, they become obsolete as technology progresses, turning into e-waste not to mention creating a huge energy sink. With access to advanced tech like artificial intelligence (AI), machine learning and real-time data, you can balance sustainability and environmental costs. Flexible architecture prioritises compatibility, modularity and adaptability. 
  4. Democratise Sustainability – sustainability shouldn’t be the goal of one cheerleader within your business, it should be everybody’s problem and priority. You should try to build mutual accountability, with ecological concerns being a mainstream conversation in all of your projects. Digital tools can help this, giving employees of all levels of business the ability to be involved and drive change. 
  5. Deliver True Value – data and automation can be used to improve processes while more directly addressing core customer needs. By reducing unnecessary costs to the end user, you can outprice competitors. What’s more, by prioritising direct and longer-term customer relationships, you can understand how customers really use products and deliver true value. 
  6. Be Mindful – we all need to consider how we use technology, make better decisions, and optimise our behaviour. Data can be used to analyse the sustainability of our project, processes and systems. Instead of short-term thinking, you should consider long-term impact.

The New Era of Digital Transformation

No company is able to avoid the fundamental changes required for sustainable business operations; after all, human survival depends on us all significantly cutting global emissions. This means that transformation efforts need to go further to harness the untapped possibilities of clean technology. Those who sell products may well transform to selling services, and those who sell services may look to deliver experiences. We need to consider the whole ecosystem, invest in the right partners and suppliers, collaborate and join forces to make a long-term difference. Ultimately, we are going to see whole new models of value creation and delivery. After all, reaching net zero hangs on innovation and digital will be the key driver. 

The Benefits of Reactive Programming

In a world where there are constant triggers and responding quickly can be a significant competitive advantage, reactive programming is becoming ever more common. The idea is that, instead of users needing to take action when an event occurs, software responds automatically. Reactive programming creates an efficient way to handle data updates whenever a user makes an inquiry by using automated data streams. 

While reactive programming was initially used to monitor networks, servers and software, the focus is now changing. The rapid growth of the Internet of Things (IoT) and the cloud has driven the need for increased automation. Reactive programming is used in facilities management, home automation and more. Meanwhile, the cloud has enabled reactive applications to improve scalability and reliability, further driving their growth within software development. 

What is Reactive Programming?

Reactive programming is a design model that uses asynchronous programming logic to manage real-time updates to static content. Reactions are based on events occurring, such as alerts or keystrokes, calls from workflows or system messages. Ultimately, an event is a signal that something has happened in real-time; the reaction needs to be processed in real time too, so that it happens in unison. In reactive programming, these events are thought of as streams of cohesive data signals that flow through several processing elements and can be stopped, handled or diverted along the way. Processing is time-sensitive, which requires a different programming style, and this is where reactive programming comes into its own. 

Reactive programming utilises observer and handler functions. Observer functions recognise changes as they occur and signal that they’ve taken place. Meanwhile, handler functions deal with those messages and evoke the necessary action. Software created with reactive programming needs to be resilient, scalable and able to manage variable loads, as there is no control over the number or timing of events. 

How Does Reactive Programming Work?

As we’ve touched on, reactive programming is centred around data streams or sequences of events. A stream starts with an observer function that either waits for an event to occur or triggers an event. The stream then flows from the observer to one or more handlers that manage the necessary processing. When it comes to software development, reactive programming is the process of building the observers and handlers that make up the required stream. 

Streams that are generated by observers within software either work alongside application processing or run periodically to look for a database element. When the software recognises a prescribed condition, it creates an event within the stream. The message-handling process will then determine whether a single handler or multiple handlers are required and will also monitor load-balancing amongst simultaneous handlers. Each handler can either pass the message along, end the message, generate an error or fork the message to other streams. 

The 8 Principles of Reactive Programming

Many programs treat events as an afterthought, either as operational or infrastructure problems that are managed with increasingly complex technology stacks and workarounds. However, distributed systems that incorporate reactive programming incorporate certain foundational principles that enable them to deal with events effectively in real time:

  1. Stay Responsive – always responding in a timely manner
  2. Accept Uncertainty – building reliability despite unreliable foundations
  3. Embrace Failure – designing for resilience even when things go wrong
  4. Assert Autonomy – creating components that act independently and collaboratively
  5. Tailor Consistency – balancing components to ensure availability and performance
  6. Decouple Time – processing asynchronously to avoid delays
  7. Decouple Space – creating flexibility by embracing the network
  8. Handle Dynamics – continuously adapt to changing demands and resources

The Benefits of Reactive Programming

Reactive programming changes how code is designed and written, so it’s not something that should be used without considerable thought. However, for an experienced software development team, it can offer some incredible benefits:

  • Improved Control – better control over the time it takes to respond to and process events.
  • Faster development – more consistency in the development of real-time systems, reducing development time, cost and effort.
  • Better Resilience – more control over load balancing, which in turn improves the quality of the user experience.
  • Easier Management – the management of compute elements and processing resources is more visual and easier to manage.
  • More Concise Code – instead of focusing on implementation details, developers review the interference between events that define business logic, often resulting in more compact implementations.
  • Less Code – by using reactive programming libraries, such as RxJS, developers can spend less time writing behaviours that are already built-in.
  • Reduced Costs – developers can leverage reactive libraries to solve complex problems with little effort, meaning there is no need to reinvent the wheel. 
  • Simple maintenance – with complex problems solved in a very declarative manner, code becomes more concise and straightforward and easier to maintain. 

Of course, reactive programming doesn’t come without its challenges. Adding observer processes to existing software can be extremely complicated depending on the availability of source code and if reactive systems have a huge amount of processes linked to a stream, delays can start to accumulate. However, the biggest challenge is the mindset shift for developers. 

How Can Your Business Leverage Reactive Programming?

Reactive programming has many use cases that can help improve the speed and quality of applications. This explains why the traditional target of reactive programming has involved to incorporate new technologies that have come to the forefront. 

The most common intended use of reactive programming is in applications that gather status information from networks. However, this is now converging with IoT. Today, one of the primary uses are IoT applications, with sensors creating events that control real-world reactions or, indeed, transactions. Meanwhile, any application where each keystroke or action needs to be processed and interpreted can benefit hugely from reactive programming.

Ultimately, with reactive programming, your development team can work towards creating an event-driven architecture that is more reliable, scalable and resilient. 

7 Use Cases for Big Data

Our businesses create, collect and consume an incredible amount of data, and it is multiplying rapidly. And that data can have a huge impact on business success by informing and improving decision-making, revamping and refining operations and creating new streams of revenue. It’s a resource that has unlimited potential for growth, as long as we’re able to store it and have the computing power to handle it. After all, big data is about more than size, data volume is just one of the aspects. The term also applies to the different varieties of data and the velocity and frequency of that data. Companies that are managing to master big data have the potential to create a profound impact. Here are just seven ways in which big data combined with analytics is delivering incredible business value:

1. Streamlining Business Operations

Big data analytics offers the ability to spot inefficiencies, identify areas that require improvement, and allocate resources effectively. Businesses that don’t use big data often rely on reports, ineffective dashboards and charts. The whole process is incredibly time-consuming, which impacts accuracy and the ability to view information in real time. With big data analytics, everyone in the business can have access to flexible, on-demand data insights and operations can be improved across the business. 

2. Forecasting with Greater Accuracy

Big data gives businesses the power to easily detect patterns and trends. This data can be used to improve forecasting, ensuring there are no shortages in products or hiccups in the supply chain. What’s more, it can help with budgeting and forecasting. By having a more accurate idea of the demand for a product or service, businesses are much better able to price it correctly and get the best return on investment. In a time where supply chain disruptions are rife, having the ability to accurately forecast can help to optimise inventory, shipping and logistics. 

3. Gaining a Holistic Customer View

Our digital footprints are growing exponentially as technology becomes increasingly intertwined in our daily lives. From a business perspective, all those clicks, views, sensor data, real-world data and more create an incredibly useful and insightful digital trail. Businesses that are harnessing big data effectively are analysing their customers and users and then using those insights to improve their services and meet specific user needs. Traditionally, static reports would take a long time to create and modify. With advanced analytics software, on the other hand, businesses can combine data from multiple sources and create dashboards that offer a complex view of customer behaviours and preferences. With this holistic view of their customers, businesses can make intelligent data-led decisions that can improve customer experience, enhance products and services and drive business growth. 

4. Improving Brand Loyalty

As an offshoot of creating dashboards that offer a complete view of the customers, businesses can use big data to fully understand what their customers are interested in, how they use products and services and what is standing in their way. Analysing behavioural patterns enables businesses to not just improve their products and services but to engage with their customers on a deeper level, have more meaningful conversations and build brand loyalty. There is no shortage of what can be measured and, when used in the right way, it can have a huge impact on customer acquisition and retention. 

5. Building Cyber Resilience

One of the downsides of the increased amount of data we consume and share across different devices, networks and applications is the rise in cyber threats. As our digital footprints grow, so does the attack surface. However, businesses are able to turn this on its head and tackle fraud and cybersecurity risks using big data analytics. Big data can be used to analyse behaviour internally to detect suspicious activity and also combine this with data from third-party sources. By having access to threats that haven’t even shown up yet in their own systems, businesses stand a chance of being a step ahead and can put the necessary protections in place to mitigate the risk of a cyberattack. 

6. Delivering Improved Personalisation

One of the most well-documented uses of big data is for product recommendations. Music and video streaming services have been taking advantage of this for some time, with massive customer behaviour databases, they’re able to use algorithms to predict customer interests and to optimise searches and recommendations. Instead of there being an overwhelming amount of choice, companies using big data can make tailored recommendations and improve customer satisfaction. Meanwhile, online advertising is taking advantage of big data analytics in a similar way. Ultimately, any company that has access to behavioural data can use it to inform decisions about what products and services to offer. 

7. Enhancing Risk Models

Business risk is complicated, which makes being able to anticipate, plan and respond to change a huge business strength. Big data can be used to improve the whole risk management process by providing early visibility into potential risks and helping to ascertain the level of risk. Big data-driven risk models can help predict everything from market risks to natural disasters with data used from a wide range of disparate sources. 

Will Your Business Harness the Power of Big Data?

While these use cases are some of the most common, they are just the beginning of what your business can achieve with big data. Every business has the power to use big data to respond to changing needs, meet evolving customer expectations and reduce business risk. On the flip side, those who don’t use big data stand to be at a disadvantage. If your business isn’t using big data to inform decision-making, then your competitors will be. 

How Software Drives Digital Transformation

The digital economy is well and truly here and to survive and thrive, businesses need to use a wide portfolio of digital assets. Newer companies sometimes have an advantage as they were born digital, but either way, all companies need to make sure their technology stack matches the speed and responsiveness of a digital world. 

While the focus of change used to be on new computers and gadgets, today it’s software that is making the biggest impact. While hardware used to drive the most innovation, over the past few years, hardware advances have started to reach their limits in terms of return on investment (ROI). Further advances will still enable new products, but today it’s software that ties technology together and creates true business value. The fact is that in today’s ever-competitive business landscape, software is the primary driver of business innovation and agility and is fundamental to delivering the benefits of digital transformation. 

What is Digital Transformation?

Digital transformation is the adoption of digital technologies in a bid to improve processes and, ultimately, increase revenue. It involves removing manual processing and modernising systems to unleash the power of technology. The end goal is to reframe operating models, work in a more efficient way and have the agility to scale to meet new business needs. 

The vast majority of businesses have plans to adopt a digital-first business strategy, which means the race to get there first is most certainly on. However, doing it in the right way is fundamental. There is little point in introducing tools and technologies into the workplace if they aren’t well integrated and fit for purpose. Moreover, there is more to transformation than technology alone. Digital transformation represents a fundamental change in the way a business operates, which means new processes and new culture need to follow suit. 

The Mindset of a Digital Business

A digital business requires a different mindset than a traditional business. After all, thinking digital first changes how a business approaches product or service delivery into the marketplace. A successful digital business is supported by market insight, collaborative leadership and, you’ve guessed it, bespoke software platforms. 

It’s incredible how much computing power is at our fingertips. In just a few short minutes, businesses are able to rent huge power from leading cloud vendors such as Google and Microsoft, and scale to meet the global demand. What differentiates these businesses, alongside the mindset change, is software. It’s enabling smaller businesses to break through and compete in new markets and existing businesses to change direction and take advantage of new opportunities. 

For digitally-minded businesses, technology is a tool for innovation rather than an end goal. After all, transformation is an ongoing process. The emphasis should always be on the business and the end-user, not technology, with software driving tangible value throughout the organisation. 

The Demand for Customised Software

Developers used to have to design foundational software and off-the-shelf products that could be used by multiple businesses and industries. Today, however, they are able to work from an ever-growing foundation of software libraries that have been created and shared within the open-source community. This gives developers a head start when it comes to innovation. By using open-source software, developers can shorten build times while increasing the pace of change. 

As software development and platform costs have become less expensive, there is a growing demand for customised software. Moreover, digital businesses that embrace customised software solutions have unprecedented opportunities to drive disruption within their industries. You only have to think about how Airbnb and Uber entirely changed traditional business structures and expanded market reach almost overnight. 

The fact is that, whether software drives efficiency, automation, competitive advantage or innovation, it puts other businesses at risk. Those who are using software to transform are taking a huge leap ahead and creating unlimited potential. 

How Bespoke Software Can Help Your Business

Digital transformation programmes have been fast-tracked since the global pandemic in 2020. Companies have learnt the benefits of using automation, removing manual processes for smarter ones and extending the capabilities of core enterprise applications. Ultimately, software has become a real champion for transforming our businesses. As such, business leaders are investing a huge amount in customised software. 

Custom-built software can help you quickly accelerate your digital transformation journey and strengthen your market position. Ultimately, software that is created specifically for your business can help you to use power fuel tech to:

  • Automate Processes – handling high-volume tasks to increase operational efficiency, with hyper-automation leading to operational excellence. 
  • Create Unique Experiences – empowering employees to work in new ways, with the focus on users, accessibility and agility whether working in a physical office or remotely.
  • Make Data-Driven Decisions – sophisticated management of data with built-in reporting and analysis. Software can help to access real-time data and detect trends in digital behaviour. By capturing, recording and conveying business data, you can eliminate guesswork and ensure business decisions are precise and measured
  • Improved Security – security outside of on-premises architecture can be a concern, especially for mission-critical or sensitive data. However, by building bespoke software solutions, security can be built in from the ground up, implementing multi-factor authentication, highly secure encryption and more to keep your data safe wherever it is. 

If you want to start transforming, then you’re going to need software on your side. Start simple and focus on one business function at a time, make incremental improvements and build confidence in your systems. In addition, be sure to involve your workforce so that you create the cultural change required to support your new software. It doesn’t have to be a complete overnight change, but with bespoke software, you can quickly increase your business capabilities, drive automation and realise greater efficiencies. With the right software solutions, you’ll be in a place where you are able to innovate and meet new opportunities and challenges. 

Why Edge Computing Matters in Software Development

Data is vital to the way our businesses work, providing insights and supporting real-time control of operations. However, the amount of data we work with has grown exponentially. With huge volumes of data collected from sensors and devices in real-time from multiple locations, working with a centralised data centre isn’t enough. That’s why, to counteract bandwidth limitations, latency issues and network disruptions, businesses are turning to edge computing architecture. 

In a time when our businesses need to meet rising customer expectations, ensure reliability and performance and reduce costs, edge computing is rapidly growing in popularity. Edge computing enables businesses to meet these goals by delivering localised computing power. Ultimately, data can often be more efficiently processed when the power is close to the thing that is generating it. With the rapid expansion of the Internet of Things (IoT) and more processing power being available on mobile and embedded devices, edge computing is going to see huge growth. While, at the time of writing, only 10% of data is created and processed outside of traditional data centres, over the next two years, it’s predicted that it’ll rise to 75%. Edge computing is set to have a profound impact on software development, with more use cases, projects and applications than ever.  

What is Edge Computing?

Using traditional computing methods, data produced at an endpoint is moved to the corporate network to be worked upon by an enterprise application, with results then sent back to the endpoint. The technique works well for client-server computing for many business applications. However, with an increasing number of devices and volume of data, this traditional data centre infrastructure isn’t able to cope; it puts too much pressure on the global internet, which can experience congestion and disruption. So, while we often associate edge computing with IoT applications, it is relevant to many software development projects. The edge is, in simple terms, computing as close as possible to the origin of the information.

How Does Edge Computing Work?

The aim of edge computing is to push workloads that usually run on data centres onto user devices and closer to the source of the data itself. Instead of transmitting data to a central data centre to be processed and analysed, work is performed in situ. This applies to many situations, whether it’s a retail store or a smart city. To make edge computing work, the computing power is often deployed in strengthened enclosures that protect it from extremes of temperature and other environmental conditions. Only the result of the computing work is returned to the main data centre to deliver insights, predictive maintenance capabilities and more. 

A lot of the internet already works on a similar model to edge computing, using Content Delivery Networks (CDNs). Examples include Cloudflare and AWS CloudFront; they bring content closer to users and offload traffic from core servers. However, unlike a CDN that operates with assets like images, videos and photos, edge computing uses servers and applications with their own custom code and logic that can be executed to make decisions and process data. In this way, an edge computing service provider offers a more customisable CDN, where an application’s custom code can be run on its servers. 

The Benefits of Edge Computing

Edge computing has many benefits to offer both software developers, businesses and end users. First and foremost, users get a better experience in terms of reliability and performance. Businesses, meanwhile, can save on costs and gain more fine-grained control over resource consumption. The most notable benefits of edge computing include:

  • Greater Processing Speed – speed is the single biggest advantage of edge computing; by moving workloads closer to devices and their users, data has to travel less far and cross fewer networks, and this can result in better response times and reduced latency. 
  • Increased Reliability – by having fewer network hops, there is less risk of internet congestion, leading to network problems that could impact application performance. As a result, applications that process on the edge are more reliable.
  • Data Sovereignty and Privacy – with data on location, it can be easier to comply with localised security regulations. Meanwhile, privacy-minded customers who want to own their own data are given greater control. 
  • Improved Security Practices – leading edge providers offer security infrastructure that can help to mitigate cyber attacks at the edge and reduce the possibility of business disruptions, ensuring applications are protected. At the edge, only data generated there is vulnerable, so other parts of the system can function. In addition, sensitive data is processed at the edge instead of being sent across networks. 
  • Scalability – with edge computing, developers are able to spin up workloads dynamically and scale them up or down based on demand. This keeps costs under control and also keeps latency low. 
  • Reduced costs – there is less need to transfer data to the cloud when using edge computers, meaning that businesses have fewer operational expenses. In addition, the bandwidth required to handle the data transfer is reduced as the data is analysed, classified and composed in situ before it is sent. 

Edge computing is growing rapidly to meet the demands of developing applications in remote locations and enabling localised computing capabilities. As such, businesses need to make plans to meet the opportunities and challenges of managing data in edge environments. There is the chance to improve efficiency and data security, but businesses need developers who are able to capture the opportunities of the edge. 

How Can Your Business Take Advantage of Edge Computing?

Most software applications could benefit from edge computing, especially when latency is important. Ultimately, moving the computing power as close as possible to the application can make it feel faster and more enjoyable to use. Some of the many services that edge computing can enable include remote surgery, augmented reality, the Internet of Things and more. With edge computing, developers can maximise the customer experience without sacrificing security or increasing costs, delivering fast, reliable and secure apps that can lead to an increased return on investment.

Serverless Kubernetes with Azure Container Apps

What are Azure Container Apps

Please note* as of writing Azure Container Apps is a preview feature. It is not ready for production applications.

Do you love containers but don’t have the time to manage your own Kubernetes platform? Azure Containers Apps is for you.

We all love containers their small, portable, and run anywhere. Azure Containers Apps are great if you just want to run some containers but that’s just the beginning. With Container Apps, you enjoy the benefits of running containers while you leave behind the concerns of manual cloud infrastructure configuration and complex container orchestrators. 

Azure Container Apps is serverless, so you just need to worry about your app code. You can use Azure Container Apps for public-facing endpoints, background processing apps, event-driven processing, and running microservices.

Azure Container Apps is built on top of Kubernetes which means some of the open-source foundations are available to you, like Dapr and KEDA. Something to note* is that you do not have access to K8s commands in Azure Containers Apps. As we love to do in modern architectures we can support different technologies, like node and .net at the same time. We can support HTTP, Microservices communication, event processing and background tasks. With Container Apps many of the requirements you have for Modern apps are built-in like robust auto-scaling, ingress, versioning, configuration, and observability. With Azure Container Apps you can use all the good parts of K8s without the hard parts.

Where does this fit in Azure?

In Azure, we already have AKS, ACI, Web Apps for containers, and Service Fabric. Why do we need something new? In a very short summary:
-Azure Kubernetes Service (AKS) is a fully managed Kubernetes option in Azure, the full cluster is inside your subscription which means you also have power, control, and responsibility. Eg. you still need to maintain the cluster with upgrades and monitoring.
-Container Instances allow you to run containers without running servers. It’s a lower-level building block but concepts like scale, load balancing, and certificates are not provided with ACI containers.
-Web Apps for containers (Azure App Service) is fully managed hosting for web applications run from code or containers. It optimised for running web applications and not for the same scenarios Container Apps eg Microservices.

So Azure Container Apps are designed for when you want to multiple containers, with K8s like funtionality without the hassle of managing K8s.

You can find more details on the differences here.

As you can see Azure Container Apps looks like a compelling offer for many Azure microservices scenarios. At XAM in our Azure Consulting practice, we are extremely excited about the future of Azure Container Apps.

Getting Started

The great thing about Azure Containers Apps is that it’s really easy to get started. In this post we are going to assume you have an Azure subscription.

If you haven’t already you need to install the Azure cli.

brew update && brew install azure-cli

Once you’ve completed the installation then you can login to Azure

az login

Then you can easily install the Azure Container Apps cli

az extension add \
  --source https://workerappscliextension.blob.core.windows.net/azure-cli-extension/containerapp-0.2.2-py2.py3-none-any.whl

In order to run a container app, we need both a log analytics workspace and a container apps environment. So first let’s setup the log analytics workspace.

 az monitor log-analytics workspace create \
  --resource-group InternalProjects \
  --workspace-name demoproject-logs    

Then we can get the workspace client id and secret of the workspace.

LOG_ANALYTICS_WORKSPACE_CLIENT_ID=`az monitor log-analytics workspace show --query customerId -g InternalProjects -n demoproject-logs -o tsv | tr -d '[:space:]'`
LOG_ANALYTICS_WORKSPACE_CLIENT_SECRET=`az monitor log-analytics workspace get-shared-keys --query primarySharedKey -g InternalProjects -n demoproject-logs -o tsv | tr -d '[:space:]'`

Now that we have a log analytics workspace we can create our Azure Container Apps environment. An Environment is a secure boundary around groups of container apps.

az containerapp env create \
  --name containerappsdemo \
  --resource-group InternalProjects\
  --logs-workspace-id $LOG_ANALYTICS_WORKSPACE_CLIENT_ID \
  --location canadacentral

Now that we have our Container Apps Environment we can easily run a container. Rather than make our own image we can use something already built, in this case I’m going to use the docker images of nopcommerce (which is a .net eCommerce platform). You can see the image here: https://hub.docker.com/r/nopcommerceteam/nopcommerce:latest

az containerapp create \
  --name nop-app \
  --resource-group XAMInternalProjects \
  --environment containerappsdemo \
  --image docker.io/nopcommerceteam/nopcommerce \
  --target-port 80 \
  --ingress 'external' \
  --query configuration.ingress.fqdn

After we run our command we get the public location of the running container apps and can easily browse and see this running.

Running multiple containers and multiple technologies

Now that we have our environment we can easily run more containers. We already know with containers we get this for free but for fun let’s use multiple technologies, so why not now run a nodejs application next to our .net app. In this case I’ve also used another pre-built container which is a hello world nodejs app, https://mcr.microsoft.com/azuredocs/aci-helloworld

az containerapp create \
  --name nodeapp \  
  --resource-group InternalProjects \
  --environment containerappsdemo \
  --image mcr.microsoft.com/azuredocs/aci-helloworld  \ 
  --target-port 80 \
  --ingress 'external' \
  --query configuration.ingress.fqdn

After we run our command we are returned the public url of the Container App, we can see below the url of

If we then open that url in our browser, we can see we’ve got our running node app and with a secure endpoint.


Azure Containers Apps allows you to build K8s style applications without the hassle of managing K8s. This technology has a huge amount of potential to help all sizes of companies deliver more value to their customers faster and more efficiently, by cutting down on the management/configuration required for these types of applications.

I’m really excited to see Azure Container Apps evolve and I would love to one day use this in a production scenario.

.NET MAUI – Exploring Overlays – Part 2

This is post #4 in a series called ‘.NET MAUI Source of Truth’.

About Source Of Truth – As any developer knows, source code is the purest form of truth in working software. So I’ve decided the best way to get deep into .NET MAUI is to look at the source code.

In my last posts, we explored the .NET MAUI codebase learning about the new Windows functionality, and then managed to get a demo of them working in Preview11. Then we’ve dived into overlays in .NET MAUI.

After writing my previous post on Overlays I found an interesting piece of code in the .NET MAUI codebase. This code was basically a advanced implementation of a WindowsOverlay. This was called the VisualDiagnosticsOverlay and I also managed to find a good sample project called DrasticOverlay created by https://twitter.com/drasticactionSA.

This sample project is a good example of what can be done with overlays. This sample project has example of Overlays, Background Overlays, Hit Detection Overlays, Videos, Menus, Loading, Drag and Drop and more.

Looking through this codebase we can see the different overlays that have been created, see below.

In this case, let’s dig into the drag and drop overlay to see how these awesome overlays are working. The basic implementation of an overlay is a partial class that inherits from WindowOverlay. The partial class combined with multi-targeting allows for common code and native implementations to nicely sit side by side.

Below, I’ve included the DragAndDropOverlay code. In this, we can see our partial class and inheritance from WindowOverlay. Overall it’s a fairly simple class that has a drop element, which is a IWindowOverlayElement. In this case the element is using the drawing capabilities available in .NET MAUI. Then a simple event handler and IsDragging property.

public partial class DragAndDropOverlay : WindowOverlay
    DropElementOverlay dropElement;
    bool dragAndDropOverlayNativeElementsInitialized;

    internal bool IsDragging
        get => dropElement.IsDragging;
            dropElement.IsDragging = value;

    public DragAndDropOverlay(IWindow window)
        : base(window)
        this.dropElement = new DropElementOverlay();

    public event EventHandler<DragAndDropOverlayTappedEventArgs>? Drop;

    class DropElementOverlay : IWindowOverlayElement
        public bool IsDragging { get; set; }
        // We are not going to use Contains for this.
        // We're gonna set if it's invoked externally.
        public bool Contains(Point point) => false;

        public void Draw(ICanvas canvas, RectangleF dirtyRect)
            if (!this.IsDragging)

            // We're going to fill the screen with a transparent
            // color to show the drag and drop is happening.
            canvas.FillColor = Color.FromRgba(225, 0, 0, 100);

As we can see in the previous image there are native implementations on Windows, iOS and Android. Let’s now take a look at the iOS partial class, this class is named DragAndDropOverlay.iOS.cs. You can see the full file over here:

Below is the Initialize() method on the iOS implementation. There’s a native view called DragAndDropView and this is added as a subview on the nativeWindow?.

// We're going to create a new view.
// This will handle the "drop" events, and nothing else.
dragAndDropView = new DragAndDropView(this, nativeWindow.RootViewController.View.Frame);
dragAndDropView.UserInteractionEnabled = true;

Below we can see part of the DragAndDropView which is implemented in the iOS code. We can see that it’s making use of native APIs eg UIView and IUIDropInteractionDelegate.

class DragAndDropView : UIView, IUIDropInteractionDelegate
    DragAndDropOverlay overlay;

    public DragAndDropView(DragAndDropOverlay overlay, CGRect frame)
        : base(frame)
        this.overlay = overlay;
        this.AddInteraction(new UIDropInteraction(this));

    public bool CanHandleSession(UIDropInteraction interaction, IUIDropSession session)
        Console.WriteLine($"CanHandleSession ({interaction}, {session})");

        return session.CanLoadObjects(new Class(typeof(UIImage)));

In looking at the majority of the implementations they all follow a similar pattern to the DragAndDrop Overlay. As mentioned previously you can implement Overlay on multiple levels, so you can use the Cross-Platform drawing methods available in .NET MAUI and you can also do native implementations for each platform.

If you are interested in overlays I would recommend taking a look at this cool sample project – DrasticOverlay and the VisualDiagnosticOverlay from the .NET MAUI codebase.

Overall it’s nice to see that we can implement this level of functionality within overlays in .NET MAUI. Ideally I would like to see support for .NET MAUI controls inside overlays, presently I’m not sure how we could implement this but I suspect it would be possible.