IPS/IDS. Systems for Detecting and Preventing Cyber Attacks

Latency, latency, latency! It has always been a problem of the Internet. It was, it is, and it probably will be. Delivering data from one geographic point to another takes time. However, latency can be reduced. This can be achieved in several ways: Reduce the number of intermediate nodes on the data path from the remote server to the user. The fewer the handlers, the faster the data reaches the destination. But this is hardly feasible. The global Internet continues to grow and become more complex, increasing the number of nodes. More nodes = more power. That’s the global trend. Evolution! Instead of regularly sending data over long distances, we can create copies of it on nodes closer to the user. Fortunately, the number of network nodes keeps growing, and the topology spreads ever wider. Eureka! The latter option seems like an absolute solution. With a large number of geographically distributed nodes, it's possible to create a kind of content delivery network. In addition to the main function—speeding up loading—such a network brings several other benefits: traffic optimization, load balancing, and increased fault tolerance. Wait a second! That's exactly what a CDN is—Content Delivery Network. So, let’s let this article explain what a CDN is, how it works, and what problems it solves.  What is a CDN? A CDN (Content Delivery Network) is a distributed network of servers designed to accelerate multimedia content delivery (images, videos, HTML pages, JavaScript scripts, CSS styles) to nearby users. Like a vast web, the CDN infrastructure sits between the server and the user, acting as an intermediary. Thus, content is not delivered directly from the server to the user but through the powerful "tentacles" of the CDN. What Types of Content Exist? Since the early days of the Internet, content has been divided into two types: Static (requires memory, large in size). Stored on a server and delivered to users upon request. Requires sufficient HDD or SSD storage. Dynamic (requires processing power, small in size). Generated on the server with each user request. Requires enough RAM and CPU power. The volume of static content on the Internet far exceeds that of dynamic content. For instance, a website's layout weighs much less than the total size of the images embedded in it. Storing static and dynamic content separately (on different servers) is considered good practice. While heavy multimedia requests are handled by one server, the core logic of the site runs on another. CDN technology takes this practice to the next level. It stores copies of static content taken from the origin server on many other remote servers. Each of these servers serves data only to nearby users, reducing load times to a minimum. What Does a CDN Consist Of? CDN infrastructure consists of many geographically distributed computing machines, each with a specific role in the global data exchange: User. The device from which the user sends requests to remote servers. Origin Server. The main server of a website that processes user requests for dynamic content and stores the original static files used by the CDN as source copies. Edge Node. A server node in the CDN infrastructure that delivers static content (copied from the origin server) to nearby users. Also called a Point of Presence (PoP). A single CDN infrastructure simultaneously includes many active users, origin servers, and edge nodes. What Happens Inside a CDN? First, CDN nodes perform specific operations to manage the rotation of static content: Caching. The process of loading copies of content from the origin server to a CDN server, followed by optimization and storage. Purge (Cache Clearing). Cached content is cleared after a certain period or on demand to maintain freshness on edge nodes. For example, if a file is updated on the origin server, the update will take some time to propagate to the caching nodes. Second, CDN nodes have several configurable parameters that ensure the stable operation of the entire infrastructure: Time to Live (TTL). A timeout after which cached content is deleted from an edge node. For images and videos, TTL can range from 1 day to 1 year; for API responses (JSON or XML), from 30 seconds to 1 hour; HTML pages may not be cached at all. CDN nodes usually respect the HTTP Cache-Control header. Caching Rule. A set of rules that determines how an edge node caches content. The primary parameter is how long the file remains in the cache (TTL). Restriction. A set of rules on the edge node that moderates access to cached content for security purposes. For example, an edge node may serve requests only from nearby IP addresses or specific domains. Thus, static content flows from the origin server through edge nodes to users, cached based on specific caching rules, and cleared once the TTL expires. Meanwhile, access restrictions are enforced on every edge node for security. How Does a CDN Work? Let's see how a CDN works from the user's perspective. We can divide the process into several stages: User Request Execution. When a user opens a website, the browser sends requests to CDN servers specified in HTML tags or within JavaScript code (such as Ajax requests). Without a CDN, requests would go directly to the origin server. Finding the Nearest Server. Upon receiving the request, the CDN system locates the server closest to the user. Content Caching. If the requested content is in the cache of the found CDN server, it is immediately delivered to the user. If not, the CDN server sends a request to the origin server and caches the content. Data Optimization. Content copies on CDN servers are optimized in various ways. For example, files can be compressed using Gzip or Brotli to reduce size. Content Delivery. The optimized and cached content is delivered to the user and displayed in their browser. For instance, if a website’s origin server is in Lisbon and the user is in Warsaw, the CDN will automatically find the nearest server with cached static content—say, in Berlin. If there is no nearby CDN server with cached content, the CDN will request the origin server. Subsequent requests will then be served through the CDN. The straight-line distance from Warsaw to Lisbon is about 2800 km, while the distance from Warsaw to Berlin is only about 570 km. Someone unfamiliar with networking might wonder: “How can a CDN speed up content delivery if data travels through cables at the speed of light—300,000 km/s?” In reality, delays in data transmission are due to technical, not physical, limitations: Routing. Data passes through many routers and nodes, each adding small delays from processing and forwarding packets. Network Congestion. High traffic in some network segments can lead to delays and packet loss, requiring retransmissions. Data Transmission Protocols. Protocols like TCP include features such as connection establishment, error checking, and flow control, all of which introduce delays. Thus, the difference between 2800 km and 570 km is negligible in terms of signal propagation. But from a network infrastructure perspective, it makes a big difference. Moreover, a CDN server in Berlin, finding no cached content, might request it not from the origin server but from a neighboring CDN node in Prague, if that node has the content cached. Therefore, CDN infrastructure nodes can also exchange cached content among themselves. What Types of CDN Exist? There are several ways to classify CDNs. The most obvious is based on the ownership of the infrastructure: Public. The CDN infrastructure is rented from a third-party provider. Suitable for small and medium-sized companies. Private. The CDN infrastructure is deployed internally by the company itself. Suitable for large companies and IT giants. Each type has its own pros and cons:   Public Private Connection speed High Low Initial costs Low High Maintenance complexity Low High Cost of large-scale traffic High Low Control capabilities Low High Dependence on third parties High Low Many CDN providers offer free access to their infrastructure resources to attract users. However, in such cases, there are limitations on: Server capacity Traffic volumes Geographical coverage Advanced configuration options Paid CDN providers use various pricing models: Pay-as-you-go. Costs depend on the volume of data transferred, measured in gigabytes or terabytes. Flat-rate pricing. Costs depend on the chosen plan with a fixed amount of available traffic. Request-based pricing. Costs depend on the number of user requests made. Deploying your own CDN infrastructure is a serious step, usually justified by strong reasons: Public CDN costs exceed the cost of running your own infrastructure. For example, high expenses due to massive multimedia traffic volumes. The product hits technical limitations of public CDNs. For example, heavy network loads or a specific user geography. The project demands higher reliability, security, and data privacy that public CDNs cannot provide. For example, a government institution or bank. Here are a few examples of private CDN networks used by major tech companies: Netflix Open Connect. Delivers Netflix’s streaming video to users worldwide. Google Global Cache (GGC). Speeds up access to Google services. Apple Private CDN. Delivers operating system updates and Apple services to its users. What Problems Does a CDN Solve? CDN technology has evolved to address several key tasks: Faster load times. Files load more quickly (with less latency) because CDN servers with cached static content are located near the user. Reduced server load. Numerous requests for static content go directly to the CDN infrastructure, bypassing the origin server. Global availability. Users in remote regions can access content more quickly, regardless of the main server’s location. Protection against attacks. Properly configured CDN servers can block malicious IP addresses or limit their requests, preventing large-scale attacks. Traffic optimization. Static content is compressed before caching and delivery to reduce size, decreasing transmitted data volumes and easing network load. Increased fault tolerance. If one CDN server fails or is overloaded, requests can be automatically rerouted to other servers. The CDN, being a global infrastructure, takes over nearly all core responsibilities for handling user requests for static content. What Are the Drawbacks of Using a CDN? Despite solving many network issues, CDNs do have certain drawbacks: Costs. In addition to paying for the origin server, you also need to pay for CDN services. Privacy. CDN nodes gain access to static data from the origin server for caching purposes. Some of this data may not be public. Availability. A site’s key traffic may come from regions where the chosen CDN provider has little or no presence. Worse, the provider might even be blocked by local laws. Configuration. Caching requires proper setup. Otherwise, users may receive outdated data. Proper setup requires some technical knowledge. Of course, we can minimize these drawbacks by carefully selecting the CDN provider and properly configuring the infrastructure they offer. What Kind of Websites Use CDNs? In today’s cloud-based reality, websites with multimedia content, high traffic, and a global audience are practically required to use CDN technology. Otherwise, they won’t be able to handle the load effectively. Yes, websites can function without a CDN, but the question is, how? Slower than with a CDN. Almost all major websites, online platforms, and services use CDNs for faster loading and increased resilience. These include: Google Amazon Microsoft Apple Netflix Twitch Steam Aliexpress However, CDNs aren’t just for the big players — smaller websites can benefit too. Several criteria suggest that a website needs distributed caching: International traffic. If users from different countries or continents visit the site. For example, a European media site with Chinese readers. Lots of static content. If the site contains many multimedia files. For example, a designer’s portfolio full of photos and videos. Traffic spikes. If the site experiences sharp increases in traffic. For example, an online store running frequent promotions or ads. That said, there are cases where using a CDN makes little sense and only complicates the web project architecture: Local reach. If the site is targeted only at users from a single city or region. For example, a website for a local organization. Low traffic. If only a few dozen or hundreds of users visit the site per day. Simple structure. If the site is a small blog or a minimalist business card site. Still, the main indicator for needing a CDN is a large volume of multimedia content. Where Are CDN Servers Located? While each CDN’s infrastructure is globally distributed, there are priority locations where CDN servers are most concentrated: Capitals and major cities. These areas have better-developed network infrastructure and are more evenly spaced worldwide. Internet exchange points (IXPs). These are locations where internet providers exchange traffic directly. Examples include DE-CIX (Frankfurt), AMS-IX (Amsterdam), LINX (London). Data centers of major providers. These are hubs of major internet backbones that enable fast and affordable data transmission across long distances. The smallest CDN networks comprise 10 to 150 servers, while the largest can include 300 to 1,500 nodes. Popular CDN Providers Here are some of the most popular, large, and technologically advanced CDN providers. Many offer CDN infrastructure as an add-on to their cloud services: Akamai Cloudflare Amazon CloudFront (AWS CDN) Fastly Google Cloud CDN Microsoft Azure CDN There are also more affordable options: BunnyCDN KeyCDN StackPath Some providers specialize in CDN infrastructure for specific content types, such as video, streams, music, or games: CDN77 Medianova Choosing the right CDN depends on the business goals, content type, and budget. To find the optimal option, you should consider a few key factors: Goals and purpose. What type of project needs the CDN: blog, online store, streaming service, media outlet? Geography. The provider's network should cover regions where your target audience is concentrated. Content. The provider should support caching and storage for the type of content used in your project. Pricing. Which billing model offers the best value for performance? In practice, it’s best to test several suitable CDN providers to find the right one for long-term use. In a way, choosing a CDN provider is like choosing a cloud provider. They all offer similar services, but the implementation always differs. Conclusion It’s important to understand that a CDN doesn’t fully store static data; it only distributes copies across its nodes to shorten the distance between the origin server and the user. Therefore, the main role of a CDN is to speed up loading and optimize traffic. This is made possible through the caching mechanism for static data, which is distributed according to defined rules between the origin server and CDN nodes.

Deep Learning (DL) is at the core of numerous technologies that millions of people use every day worldwide, including self-driving car algorithms, Netflix and Amazon Prime Video recommendations, and voice assistants like Siri or Google Assistant. Just a few decades ago this technology seemed like science fiction, and yet today it has become an integral part of our lives. Deep learning uses multilayer neural networks to reveal hidden patterns, such as recognizing facial features in a picture or deciphering text, and is inspired by the structure of the human brain. In this article, we’ll talk about the fundamentals of deep learning and its principles, learn how it works, and describe its application areas. What is Deep Learning? Deep learning is a rapidly evolving field of artificial intelligence that enables computers not just to follow commands, but to learn and solve complex problems independently while processing large volumes of data. Think of a program that can recognize a face in a picture, understand spoken language, translate between languages, or even forecast stock prices. That’s deep learning in action. The word “deep” refers to the many layers within neural networks. The more layers, the deeper and more capable the network becomes. In traditional programming, developers explicitly write out algorithms. However, deep learning allows the system to determine how to complete a task on its own. For example, to teach a system to recognize cats in images, you don’t need to define features like whiskers, paws, or tails; just show it thousands of images, and it will learn the key traits on its own. Deep learning isn’t just a technology; it’s an approach that helps machines reach a level of perception closer to human cognition. Deep Learning vs. Machine Learning Think of machine learning as the broader category. It includes any technique where computers learn from data. Classical ML includes algorithms like linear regression, decision trees, random forests, and support vector machines, which work well with structured data. But these approaches struggle with unstructured data like images, audio, or text. In those cases, human intervention is often required to manually engineer features (like edges in an image). Deep learning, on the other hand, eliminates the need for manual feature engineering. You provide raw data — pixels, sound waves, etc. — and the model figures out what's important. Its core principle is hierarchical data representation. For instance, in face recognition: The first layer might detect edges The second identifies shapes like eyes or mouths The third combines them into facial features Deeper layers recognize complete faces with unique traits How Does Deep Learning Work? Deep learning is based on artificial neural networks. These networks process input data through many layers to produce outputs such as a predicted object or a translated phrase. Here’s how the learning process works: Input: Data is fed into the neural network. Feature Detection: Each layer detects increasingly complex features. Prediction: The network produces an output (e.g., label or value). Error Correction: The result is compared with the correct answer. If there’s an error, the network adjusts using backpropagation. Iteration: This process repeats thousands or millions of times until the network becomes accurate. Key components include: Activation functions (e.g., ReLU, sigmoid): decide which signals to pass. Optimizers (e.g., stochastic gradient descent): help minimize error. Computational resources: GPUs and TPUs accelerate training dramatically. Deep Neural Networks: Structure and Principles A Deep Neural Network (DNN) typically includes: Input Layer: Receives raw data (e.g., image pixels or audio features). Hidden Layers: The core of the network where data is transformed. Output Layer: Produces final predictions (e.g., “cat” 80%, “dog” 15%). Each layer is made up of mathematical units (neurons) that multiply, sum, and activate values to pass to the next layer. Training a Neural Network: Key Steps Training a neural network is a step-by-step process similar to human learning: Data Collection: Large, diverse, and high-quality datasets are essential. Preprocessing: Clean the data, normalize it, and split into train/validation/test sets. Choosing Architecture: Decide on the network type (CNN, RNN, Transformer) and layer count. Initialization: Start with random weights for neurons. Training: Iterate through the data (epochs), updating weights via backpropagation. Evaluation: Test accuracy on unseen data to avoid overfitting. Tuning: Adjust hyperparameters (learning rate, batch size) to improve performance. This process may take hours or even days, especially for large models, but the results are often worth it. Where Is Deep Learning Used? Deep learning has touched nearly every aspect of life enabling machines to see, hear, understand, and predict. In Computer Vision A major domain for DL, using Convolutional Neural Networks (CNNs) for: Face recognition: in smartphones, security, and social media. Self-driving cars: identifying signs, pedestrians, and other vehicles. Image processing: automatic filtering, retouching, and OCR. CNNs excel at detecting spatial patterns, from small details to full scenes. In Natural Language Processing (NLP) DL is revolutionizing language tasks using RNNs and transformers for: Translation: tools like Google Translate or DeepL. Chatbots: responsive and conversational systems. Sentiment analysis: identifying emotion in text (e.g., reviews). Models like BERT and GPT-3 elevated NLP by enabling contextual understanding. In Medicine, Finance, and More DL goes beyond software, impacting real-world applications: Healthcare: diagnosing diseases from X-rays/MRIs, predicting illness, aiding drug development. Finance: fraud detection, market trend analysis, algorithmic trading. Entertainment: training AI in games, generating music, creating virtual characters. Industry: quality control, equipment failure prediction, logistics optimization. Science: genome analysis, climate modeling, physics simulations. Deep learning is a universal tool that adapts to diverse challenges. Popular Tools and Libraries for Deep Learning Powerful and user-friendly tools are essential for working with deep learning. The top favorites are TensorFlow and PyTorch. TensorFlow is a library developed by Google. It is known for its scalability and support for industrial-grade projects and is well-suited for complex systems and model deployment. PyTorch is ideal for prototyping and is very popular among researchers due to its flexibility and ease of experimentation. Both libraries support GPU computing, have active communities, and offer extensive documentation. In addition, there are other software tools available for deep learning: Keras: A high-level API for TensorFlow, simple and intuitive, ideal for beginners. MXNet: A lightweight and efficient library used by Amazon for scalable solutions. Caffe: Specialized in computer vision, widely used in academic research. Theano: One of the earliest deep learning libraries, now less popular but historically significant. You should choose your tool based on your goals: Research & experimentation: PyTorch Production & deployment: TensorFlow Beginner-friendly: Keras Specialized tasks: Caffe, MXNet Where to Start with Deep Learning Core Knowledge Needed To begin with deep learning, you should understand: Math: Linear algebra (vectors, matrices), statistics (distributions, probability), and optimization (gradients). Programming: Python is essential, along with libraries like NumPy, Pandas, and Matplotlib. Basic ML Concepts: Regression, classification, and evaluation metrics. Courses, Books, and Resources Great learning materials include: Courses: Deep Learning Specialization by Andrew Ng (Coursera) CS231n: CNNs for Visual Recognition (Stanford) Fast.ai for hands-on learning Books: Deep Learning by Goodfellow, Bengio, Courville Neural Networks and Deep Learning by Michael Nielsen (free online) Resources: YouTube: 3Blue1Brown, Sentdex Blogs: Towards Data Science, Distill.pub Where to Find Code and Projects Explore and experiment with real-world examples on: GitHub: Thousands of open-source DL projects. Kaggle: Competitions, datasets, and shared notebooks. Google Colab: Free cloud-based notebooks with GPU support. Papers with Code: Research papers with source code. The Future of Deep Learning Deep learning continues to evolve rapidly. In the near future, we can expect: Efficient models: Less data and computation (e.g., TinyML for edge devices). Quantum computing: Faster training with quantum processors. Ethics & regulation: Addressing model bias and ensuring transparency. However, challenges remain: High energy consumption (leading to financial costs) Technology accessibility for small companies Security concerns (e.g., model vulnerability to attacks) Even so, deep learning has already transformed the world and continues to unlock new frontiers. Conclusion Deep learning is more than just a technology. It’s a revolution in IT. Built on multilayered neural networks, it allows machines to find patterns in data autonomously, bringing them closer to human-like perception. From theoretical models to the core of modern tech, DL has come a long way in a short time. Despite challenges like energy demands and ethical concerns, its future remains bright, helping make machines smarter and the world more technologically advanced.

Anyone who decides to get into programming faces an important question: which language should they learn first? For a beginner unfamiliar with software development's nuances, trying to answer this alone can easily lead to mistakes. Choosing hastily comes with a risk, as you might fall into the sunk cost trap. You could end up picking a language that doesn't align with your desired direction, whether it's mobile development, game dev, or systems programming. Relearning another language later may render much of your time and effort wasted. So, it is of course much better to make the right decision early. And for this, you need at least a general understanding of how each language works: its specifics, features, areas of application. In this article, we’ll present both complex and beginner-friendly programming languages, to help  beginners make an informed choice. Why It’s Important to Choose the Right Programming Language There are several reasons why it's crucial to study the features of each language at the very beginning and pick the most suitable one: Task suitability. Every language has its own strengths. One might be better suited for a specific set of tasks than another. Your chosen development field heavily affects your tech stack, especially in today's world, where specialization is becoming more prominent. The days when operating systems, programs, and games were all written in one language are gone. Now, there's a tool for everything. Community support. Any popular programming language has an active community, extensive documentation, and plenty of frameworks and libraries. However, more complex languages (like C++) can be harder to work with regarding libraries and documentation. You should take this into account. Career prospects. Learning a high-demand language opens more job opportunities and makes it easier to find projects that align with your interests and specialization. Scalability and performance. Some tasks require special features from a language, like efficient memory management or support for parallel computing. Sometimes, these factors are critical. So, clearly understanding which language to start learning can help avoid many future issues, and at best, guide you into an exciting direction and a successful developer career. Python: The Best Starting Point Python is a high-level, interpreted programming language with dynamic typing. Dynamic typing means the variable type is determined at runtime and can change. This adds flexibility but increases the chance of errors. Static typing means a variable's type is set at compile time and can't change. Type errors are caught earlier. For example, in a dynamically typed language, you could first assign the number 7 to a variable and later assign a string like "Hello, World" to that same variable. In a statically typed language, this would cause a compile-time error. Interpreted languages execute code directly without first converting it to machine code. Compiled languages, on the other hand, convert high-level code into machine instructions, making them generally faster. Python was initially created by Dutch programmer Guido van Rossum in 1991. Today, it is maintained by the global Python Steering Council and the nonprofit Python Software Foundation. Simple Syntax Python’s key feature is its use of indentation and colons instead of curly braces to define code blocks: if True: print("One block of code") else: print("Another block of code") This simplifies the language and makes the code more visually readable, especially in Object-Oriented Programming: class Butler: def __init__(self, name): self.name = name def hello(self): print(f"The butler of this mansion welcomes you — {self.name}!") butler = Butler("Alfred") butler.hello() # Output: The butler of this mansion welcomes you — Alfred Python aims to be both clear and functional, using as few syntax elements as possible (like braces or semicolons). Versatile Applications Thanks to its clean syntax and line-by-line execution, Python can be used in a wide variety of fields: Web Development. Building the backend of web apps, handling user requests (RESTful APIs), and generating dynamic web pages. Machine Learning. Processing and analyzing large datasets, building ML models, and creating neural networks. It’s also widely used in scientific computing across physics, biology, and engineering. Automation. As a scripting language, Python is used to automate routine tasks, manage servers, and streamline DevOps workflows. Despite its power and use in large-scale infrastructure and serious applications, Python remains the most beginner-friendly programming language. Large Community and Documentation Python is used globally across industries and research, resulting in a massive community of developers, engineers, and scientists. Regular conferences like PyCon, EuroPython, and PyData foster idea-sharing and collaboration. Online platforms like StackOverflow and Reddit host extensive discussions on Python coding nuances. The official documentation provides detailed language syntax, standard libraries, and step-by-step guides with examples, covering even the most basic topics. JavaScript: The Language of Web Development JavaScript is a high-level, interpreted programming language with dynamic typing. It was developed in 1995 by Brendan Eich at Netscape. Its name's similarity to Java was a marketing decision rather than a technical one. Java was extremely popular at the time, and the name helped boost interest in the new language. The Core of Frontend Development Modern browsers come with a built-in JavaScript engine to run scripts that manipulate the DOM (Document Object Model) to dynamically change a web page’s content: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>DOM Manipulation</title> </head> <body> <div id="container"> <p>This is the original text.</p> </div> <button id="changeBtn">Change Content</button> <script> const container = document.getElementById('container'); const button = document.getElementById('changeBtn'); button.addEventListener('click', function() { const firstParagraph = container.querySelector('p'); firstParagraph.textContent = "Text changed!"; const newParagraph = document.createElement('p'); newParagraph.textContent = "A new paragraph was added to the DOM."; container.appendChild(newParagraph); }); </script> </body> </html> Thanks to JavaScript, developers can create interactive UIs for modern web apps. Scripts run directly in any browser, so no extra software is needed. This makes JavaScript one of the most accessible programming languages for beginners. A Rich Ecosystem Web development with JavaScript is a whole industry on its own. There are countless libraries and frameworks for managing web app states, such as React and Vue. But JavaScript isn’t limited to the client side. With Node.js, JavaScript can also run on servers. That's why many JavaScript applications and libraries are isomorphic, meaning they work both on the front and backend. Because of this flexibility, JavaScript is a solid first programming language, helping you become a Full Stack developer (handling both frontend and backend). Java: Stability and Demand Java is a high-level, object-oriented programming language with static typing. It was developed in 1995 by Sun Microsystems (later acquired by Oracle), led by James Gosling. Java is a compiled language. Its source code is compiled into intermediate bytecode, which is executed by the Java Virtual Machine (JVM). Since JVMs are implemented for different operating systems, Java code is cross-platform and can run on any OS without recompilation. That’s why Java’s slogan is: "Write once, run anywhere." Android Development Android is an OS with many components written in different languages. While its kernel is in C and C++, app development libraries and APIs are Java-based. This has made Java almost synonymous with mobile development, including both apps and games. For example, the popular game Minecraft was written in Java and, almost immediately after its PC release in 2011, was added to the Google Play Market as a mobile version for Android. High Performance Unlike interpreted programming languages, Java uses JIT (Just-in-Time) compilation. When an application is run, the bytecode is dynamically compiled into machine code so that frequently used code segments are optimized on the fly. On one hand, Java delivers higher performance than interpreted languages, such as JavaScript or Python. On the other hand, the indirect execution of bytecode is slower than direct execution of machine instructions in compiled languages like C or C++. Java is quite a fast language, especially considering that it runs through a virtual machine to provide strong cross-platform compatibility. Popularity in the Corporate Environment Cross-platform capabilities, application portability, predictable behavior, stability, and security are key reasons why many companies prefer Java. And of course, its rich ecosystem—libraries, frameworks, and tools—all contribute to simplifying and accelerating enterprise application development, maintenance, and updating. In contrast to Python, Java uses a strict C-like syntax: public class Greeter { private String name; // Constructor that takes a name for greeting public Greeter(String name) { this.name = name; } // Method that prints the greeting to the console public void greet() { System.out.println("Hello, " + name + "!"); } public static void main(String[] args) { // If a command-line argument is provided, use it as the name. Otherwise, default to "World" String name = (args.length > 0) ? args[0] : "World"; Greeter greeter = new Greeter(name); greeter.greet(); } } C#: A Windows Developer's Alternative C# is a high-level, object-oriented programming language with static typing. However, dynamic typing is also possible using the dynamic keyword. The C# programming language first appeared in 2002. It was created by Microsoft under the leadership of engineer Anders Hejlsberg.  Like Java, C# code is not compiled directly into machine instructions but into an intermediate representation called CIL (Common Intermediate Language) or simply IL. During program execution, the CIL code is converted via JIT compilation into native machine code optimized for the target platform. Used in .NET Development and Unity .NET is a development platform created by Microsoft for building portable applications. It can be used to develop websites, mobile apps, games, neural networks, and cloud services. The .NET ecosystem includes: Programming languages (C#, F#, VB.NET) Libraries and frameworks (for databases, servers, graphics) Runtimes C# is the main language in the .NET ecosystem. To some extent, Java and .NET can be seen as similar ecosystems. In Java, apps run on the JVM (Java Virtual Machine), while in .NET, they run on the CLR (Common Language Runtime). In both cases, code is compiled to bytecode, which is then executed on a virtual machine. Moreover, Java and C# have syntactic similarities, as both are C-style languages. Naturally, game development has also embraced C#. For instance, the popular Unity game engine uses C# as the primary scripting language for creating gameplay mechanics and scenarios. Strong Microsoft Support Microsoft plays a key role in the development of C#. This support includes the language itself, tooling, libraries, and infrastructure. C# integrates well with other Microsoft products and is tailored to the Microsoft ecosystem, although it remains cross-platform. For example, the Visual Studio IDE is best optimized for the C# compiler. A simple C# console application looks like this: using System; class Program { static void Main() { Console.Write("May I have your name?: "); string name = Console.ReadLine(); Console.WriteLine($"Welcome, {name}!"); } } C and C++: For Systems Programming C and C++ are compiled programming languages that are closely related. C++ is an extended, object-oriented version of the procedural C language. C was created at Bell Labs by Dennis Ritchie in 1972, while C++ was introduced by Bjarne Stroustrup in 1983. High Performance Unlike Python, JavaScript, and Java, C and C++ do not require an interpreter or a virtual machine. Their code is compiled directly into processor instructions. In other words, these languages are as close to the hardware as possible, allowing low-level control of system resources. That’s also why these languages are considered complex—manual control and lack of automation demand high programmer skill. Memory Management C and C++ give full control over computing resources. They do not include a garbage collector that automatically frees unused memory. This reduces overhead but increases the risk of memory leaks. Due to their performance and control, C and C++ are preferred for high-load computing, like OS kernels (Linux, Windows, macOS, Android), game engines (Unreal Engine), and financial systems. In short, C and C++ remain the go-to languages when speed and efficiency are critical. Foundation for Modern Languages Originally developed for Unix-like OS development, C became the ancestor of many modern languages. Its syntax is the foundation of many popular languages: C++, Java, C#, JavaScript, Go, Swift. Example of simple C++ code using classes: #include <iostream> #include <string> class Car { private: std::string brand; public: Car(std::string carBrand) { brand = carBrand; } void showInfo() { std::cout << "Car brand: " << brand << std::endl; } }; int main() { Car myCar("Toyota"); myCar.showInfo(); return 0; } Swift: For iOS Development Swift is a modern high-level, statically typed language that is compiled into machine instructions. Apple’s Official Language Before Swift, Apple’s main language was Objective-C, dating back to the 1980s. Despite its power, it had outdated principles and lacked support for modern syntax and safe memory handling. In 2014, Apple introduced Swift, a modern, safe, and convenient language aimed at improving code writing, safety, performance, and memory management. In short, Swift was created as Apple’s new official language for iOS, macOS, watchOS, and tvOS development. Simplified Syntax Compared to Objective-C Objective-C: NSString *name = @"John"; NSInteger age = 25; NSArray *fruits = @[@"Apple", @"Banana", @"Orange"]; - (void)greet:(NSString *)name { NSLog(@"Hello, %@", name); } [self greet:@"Alice"]; Swift: let name = "John" var age = 25 let fruits = ["Apple", "Banana", "Orange"] func greet(name: String) { print("Hello, \(name)") } greet(name: "Alice") As a result, Swift has cleaner and more understandable syntax, which means faster development. A Great Choice for Mobile Development Swift is optimized for Apple’s custom chips. It’s the main language for developing native iOS applications and games. Apple actively supports and develops the Swift ecosystem, and it is fully integrated into Xcode, Apple’s official IDE. Go: A Modern Language from Google Go, or Golang, is a high-level, statically typed programming language designed with concurrency in mind. It was developed in 2007 by Google engineers Robert Griesemer, Ken Thompson, and Rob Pike. Google created Go to address speed, concurrency, and development convenience issues found in other languages, like: C++: Powerful but complex Java: Bytecode execution is slower than native Python: Simple but slow due to interpretation As a company focused on cloud services, Google made Go with server-side development in mind. Simplicity and Minimalism Go has automatic garbage collection, a simple syntax, and convenient abstractions, but it's not a classical OOP language. There are no classes, no this keyword, no method/operator overloading. Instead, Go uses structs with methods attached to them: package main import "fmt" type Person struct { Name string Age int } func (p Person) Greet() { fmt.Println("Hi, my name is", p.Name) } func main() { person := Person{Name: "Anna", Age: 35} person.Greet() } Go minimizes complexity and accelerates development by unifying syntax: No classes/inheritance: use structs and interfaces instead. No method/operator overloading: one name per method. No exceptions: use explicit error checks Exceptions are errors thrown by an application during execution that can be caught and handled by user-written code without terminating the program. At first glance, such simplification may seem to limit the programmer’s capabilities. However, in reality, a strict definition of application logic provides greater flexibility in possible implementations and solutions. This is likely why Go, along with Python and JavaScript, is considered one of the best programming languages for beginners. High Performance Go code is compiled to machine instructions. The lack of heavy features like overloading and exceptions makes Go programs high-performing. The garbage collector is optimized for minimal delays. Instead of OS level threads, Go uses goroutines—lightweight threads that use only a few kilobytes of memory and can be spawned in large numbers. Great for Server-Side Development Compiled code, concurrency support, and minimal syntax make Go ideal for backend development. Built-in packages for web servers, networking, databases, and encoding (like net/http, database/sql, encoding/json) allow out-of-the-box server app development. Which Programming Language Should You Choose First? So. When choosing a programming language, several important factors should be taken into account: Development field. In what area will you be working? Web applications, systems programming, game development? And if it’s game development, what kind? Mobile or desktop? Or maybe even console games? Future tasks. The tasks you’ll face will depend on the area of development. But the type of tasks can also vary. For example, web development includes both frontend and backend. In game development, there’s engine development and gameplay mechanics creation. Entry threshold. Depending on personal aptitudes and existing skills, learning a specific programming language will be different for every developer. For instance, effective use of C and C++ requires deeper computer science knowledge: understanding memory management, algorithm complexity, and mathematical computations. The showcased languages can be compared across several key parameters: Language Syntax Complexity Execution Model Type System Year Released Official Site Primary Field Additional Knowledge Python Low Interpretation Dynamic 1991 python.org Data Analysis Machine Learning, Big Data JavaScript Low Interpretation Dynamic 1995 - Web Development Layout, Network Protocols Java Medium Compilation Static 1995 java.com Mobile Development Android, DevOps C# Medium Compilation Static 2002 microsoft.com Desktop Development .NET C/C++ High Compilation Static 1972 / 1985 isocpp.org Systems Programming Mathematics Swift Medium Compilation Static 2014 swift.com Mobile Development macOS, iOS Go Medium Compilation Static 2012 go.dev Servers, Microservices RESTful APIs, Containerization Learning the syntax of a specific language is best done with the help of books.  You can clarify various details through tutorial articles and videos. And when problems arise during learning or work tasks, you can ask questions on platforms like StackOverflow. Ultimately, the choice always comes down to personal preference. In the long run, it’s better to pursue something you are actually interested in rather than just something profitable. Otherwise, you'll just burn out.