Software development involves using computer science to make, launch, and manage different types of software. These projects can be well-established, like the software on your computer, or new, like apps for wearables or the cloud. 

The world of software is always changing with new methods, tools, and languages. If you're not familiar with software, it might seem like it's just about "writing code." However, if you want to work with developers or create something, you need to understand the different types of software development.

Each category in software development has its own set of languages and frameworks tailored to its unique requirements. Mastering these nuances and complexities often takes years, leading skilled developers to specialize in particular types of software development based on their preferences and expertise.

This article is here to help you understand the main parts of software development, including the languages and frameworks used in everyday situations. This way, even if you're not a developer, you can get a better grasp of what's happening in the world of software.

In this article, we will cover most common types of software development, including:

• Web application development 
• Mobile application development 
• Data science 
• Video game development 
• Desktop Application development 
• Cloud computing 
• DevOps 
• Blockchain

Web Application Development

Web applications, or web apps, are interactive software accessible through a web browser. Unlike apps installed on a device, web apps are stored on a remote server. Users can access them from any device with an internet connection, making them versatile and easy to update.

In web app architecture, there is both a front end and back end each requiring a different kind of software.

Front end

Frontend development involves creating the visible part of a website or application that users interact with directly. It includes designing and implementing the user interface, ensuring a seamless and visually appealing experience. Frontend developers use languages like HTML, CSS, and JavaScript to build the look and feel of web pages, making them responsive and user-friendly across various devices and browsers.

Backend

Backend development focuses on the server-side of websites and applications. It involves building and maintaining the behind-the-scenes functionality that powers the frontend. Backend developers work with databases, server, and application logic, ensuring data is processed, stored, and delivered efficiently. They use languages such as Python, Java, or Ruby, and frameworks like Django or Node.js to create the server-side components that support the overall functionality of the software.

Progressive Web Apps

Progressive web apps (PWAs) are a type of application software delivered through the web and built using common web technologies such as HTML, CSS, and JavaScript. They offer an app-like experience to users, providing features like offline access, push notifications, and responsiveness across various devices. PWAs are designed to work on any browser and can be installed on the user's device, offering the advantages of both web and native applications.

Checkout our Web Development Services to learn about how we build websites and other web applications for our clients.

Mobile App development

Mobile application development, or creating apps for phones and tablets, involves making software that you download to your device. Most of these apps need the internet to work, like Instagram needing it to show new pictures. However, some apps, like games, can be used without the internet. Because there are many types of devices and systems, developers need to decide if their app should work on just one type of device (like iPhone or Android) or on many different devices.

Native App development

Native application development involves creating software applications specifically tailored for a particular platform or operating system. It utilizes platform-specific programming languages (e.g., Swift or Objective-C for iOS, Java or Kotlin for Android) and provides direct access to platform APIs, resulting in optimized performance and native user experiences. Native applications are typically distributed through platform-specific app stores. While offering superior performance, native development may require separate efforts for different platforms. Cross-platform frameworks, such as React Native or Flutter, aim to balance code reusability and performance.

Cross Platform Development

Hybrid development aims to blend the advantages of native and cross-platform apps. In technical terms, a hybrid app is essentially a web application enclosed in a native shell, requiring users to download it. By leveraging native elements, hybrid apps enhance the user experience. While hybrid apps offer a quicker and more straightforward development process compared to native apps, the integration of cross-platform and native development may extend the timeline and introduce potential performance challenges.

Hybrid app development

Hybrid app development involves creating applications that can run on multiple platforms, such as iOS, Android, and web browsers. These apps are built using a combination of web technologies like HTML, CSS, and JavaScript, wrapped in a native container.

Check out how we build intuitive and scalable mobile apps for iOS and Android.

Database Development

Database development is a critical aspect of software engineering that involves the design, implementation, and management of databases to efficiently store, organize, and access data. It begins with database design, where the structure of the database, including tables, columns, keys, and relationships, is defined based on the application's requirements.

Data modeling is then used to create a conceptual representation of how data should be organized and how entities relate to each other. Database management systems (DBMS) play a crucial role in interacting with users, applications, and the database itself to capture, analyze, and manage data

Querying, using languages like SQL, is essential for retrieving, updating, and managing data within the database. Security measures are also implemented to protect data from unauthorized access and breaches. Additionally, tasks such as indexing, performance tuning, backup, and recovery are essential for ensuring the database operates efficiently and reliably.Overall, effective database development is vital for businesses to leverage their data for decision-making and operational efficiency.

SQL (Structured Query Language) and NoSQL (Not Only SQL) are two major categories of database management systems, each with its own characteristics and use cases.

SQL

SQL databases are relational databases that use structured query language (SQL) for defining and manipulating data. They are based on a table-like structure where data is stored in rows and columns. SQL databases are known for their strong consistency, ACID (Atomicity, Consistency, Isolation, Durability) properties, and ability to handle complex queries. Examples of SQL databases include MySQL, PostgreSQL, and Oracle Database. SQL databases are typically used in applications where data is highly structured and relationships between different data entities are well-defined.

NO-SQL

NoSQL databases are non-relational databases that can store and retrieve data in a flexible, schema-less manner. They are designed to handle large volumes of unstructured or semi-structured data and are highly scalable and available. NoSQL databases are categorized into several types, including document stores, key-value stores, wide-column stores, and graph databases. Examples of NoSQL databases include MongoDB, Cassandra, and Redis. NoSQL databases are often used in applications where flexibility, scalability, and high availability are more important than strong consistency and complex querying capabilities.

Data Science

Data Science is an interdisciplinary field that combines programming, statistical analysis, and domain-specific knowledge to extract meaningful insights from data. Drawing on methods from mathematics, statistics, information science, and computer science, it focuses on manipulating, analyzing, and visualizing data to inform decision-making and develop predictive models.

Its applications span diverse sectors, including healthcare, finance, and marketing, where it plays a crucial role in revealing hidden patterns and insights. The true value of data science lies in its ability to empower businesses by making data-driven decisions and optimizing operations. As an illustration, in the banking sector, data science is employed to identify fraudulent activities. By analyzing patterns in transactional data, data scientists create predictive models to detect potentially fraudulent transactions.

This proactive approach allows for a more thorough review of flagged transactions, reducing instances of fraud and fostering greater trust among customers. Data scientists utilize various languages and frameworks, such as Python, R, Java, Scala, and Pandas for data manipulation, Scikit-learn for machine learning, TensorFlow for deep learning, and Matplotlib for data visualization. This diverse toolkit enables them to tackle a wide range of data-related challenges and contribute to advancements in various domains.

Video Game Development

The process of video game development encompasses the crafting of captivating narratives, characters, and environments, coupled with the intricate design and implementation of game mechanics and controls. This creative endeavor spans a spectrum, ranging from the creation of straightforward mobile games to the intricate development of immersive console and PC gaming experiences.

 In the realm of video game development, various languages and frameworks bring their unique strengths to the table:

•  C++ and Python: Recognized for their performance and simplicity, these languages are often employed to optimize the functionality and efficiency of games. 
• Unity and Unreal Engine: These are popular game development platforms that offer robust tools and capabilities. Unity is favored for its accessibility and versatility, while Unreal Engine is renowned for its high-end graphics and realism. 
• Blender and Maya: These tools are utilized for 3D modeling and animation, contributing to the creation of visually stunning and dynamic game environments.

Desktop Application Development

Desktop development pertains to the creation of software applications tailored for desktop and laptop computers, designed to function seamlessly within specific desktop operating systems like Windows, macOS, or Linux. These applications are meticulously crafted to optimize performance and user experience on traditional computer platforms.

• Windows Development: Proficiency in C# is imperative for creating system software, applications, or frameworks tailored for Windows development. 

• macOS Development: Development of macOS and iOS applications predominantly relies on Swift, while support for Python and Java is also available. The advent of Swift in 2014 marked the replacement of the earlier Objective-C language. 

• Linux Development: Although Linux desktop market share is modest (ranging between 1.74% and 2.18%), developing for Linux, with a focus on market share, is not widespread. Nevertheless, the Linux desktop market is witnessing growth, propelled by the Linux kernel's use in Android.

Cloud Computing

Cloud computing development entails the design and implementation of applications hosted on remote servers, accessible over the Internet. This approach offers businesses various advantages, including cost savings, enhanced scalability, and improved data security.

 The cloud computing market has been experiencing rapid expansion, with projections indicating substantial growth in 2023 as more businesses shift their services to the cloud. An illustrative example is the development of a cloud-based file storage system, enabling users to store and retrieve files from any internet-connected device.

 Commonly employed in cloud computing development are languages such as Python and Java, valued for their robust libraries and scalability. Leading industry cloud services providers like AWS and Azure, along with tools like Docker and Kubernetes for application containerization and orchestration, play pivotal roles in this dynamic landscape.

Check the latest innovations in cloud computing we use in our cloud development solutions

Devops

DevOps represents a philosophy that seamlessly incorporates automated security practices into Agile IT methodologies, embedding security within the swift and iterative development processes embraced by numerous organizations today. For successful DevOps implementation, software developers need to possess a comprehensive understanding of programming, integration, and testing, encompassing both theoretical principles and hands-on proficiency with various tools and frameworks.

CI/CD

CI/CD, or Continuous Integration and Continuous Delivery, is a software development practice that automates the building, testing, and deployment of applications. Continuous Integration ensures that code changes are regularly merged into a shared repository and automatically verified.

Continuous Delivery extends this by automating the release process, allowing software to be delivered to production more reliably and frequently. This approach enhances collaboration, accelerates development cycles, and improves overall software quality.

Here are some examples of popular CI/CD tools and platforms: 

• Jenkins: 
• GitLab CI/CD: 
• Travis CI: 
• CircleCI
• GitHub Actions
• AWS CodePipeline:
• Azure DevOps

For more details about DevOps Development, you can check how we build streamlined DevOps solutions for out clients.

Blockchain

Blockchain is a decentralized, distributed ledger technology that enables secure and transparent record-keeping of transactions across a network of computers, known as nodes. Key features of blockchain include immutability (once data is recorded, it cannot be altered), transparency (all participants can view the data), and security (data is cryptographically secured).

Transactions are grouped into blocks, which are then added to the chain in a linear, chronological order. Each block contains a cryptographic hash of the previous block, linking them together and forming a chain. Consensus algorithms, such as Proof of Work (PoW) or Proof of Stake (PoS), are used to validate transactions and ensure agreement among nodes. Blockchain technology is used in various applications, including cryptocurrencies like Bitcoin, smart contracts, supply chain management, and digital identity verification, among others.

• Bitcoin (BTC): The first and most well-known cryptocurrency, using blockchain technology for secure, decentralized transactions without the need for a central authority.
• Ethereum (ETH): A blockchain platform that enables developers to build decentralized applications (DApps) and smart contracts, expanding the use cases beyond simple transactions.
• Supply Chain Management: Blockchain is used to track the provenance and movement of goods across the supply chain, ensuring transparency and authenticity.
• Healthcare: Blockchain can securely store and share patient data, ensuring privacy and facilitating interoperability among healthcare providers.
• Digital Identity: Blockchain can be used to create and manage digital identities, providing secure and verifiable access to services.
• Voting Systems: Blockchain can be used for secure and transparent voting systems, ensuring the integrity of the voting process.
• Tokenization of Assets: Blockchain enables the creation of digital tokens representing real-world assets like real estate or art, facilitating ownership transfer and fractional ownership.

Embedded Software

Embedded software development involves creating software that is embedded into devices to control their functions. This type of software interacts closely with the hardware it runs on, often with real-time constraints and limited resources.

Commonly using languages like C and C++, developers use tools like cross-compilers and debuggers to manage tasks efficiently. Testing embedded software can be challenging due to hardware interactions, requiring techniques like simulation and emulation. Industries often have specific standards for embedded software development, making it a specialized and rewarding field.

In addition to the challenges mentioned, embedded software development also requires a strong focus on optimizing code for performance and power consumption.

For example, in automotive systems, embedded software controls functions such as engine management, brake systems, and driver assistance systems. In medical devices, embedded software manages functions like patient monitoring and drug delivery. Consumer electronics like smartphones and smartwatches use embedded software to control features such as touchscreens, sensors, and connectivity. These examples highlight the diverse range of applications and the critical role that embedded software plays in modern technology.

API Development

API development involves creating interfaces that allow software applications to communicate with each other. There are different types of APIs, such as RESTful APIs, GraphQL APIs, and SOAP APIs, each with its own approach to defining operations and data formats.

For example, a RESTful API for a blogging platform might have endpoints like /posts to get all blog posts and /posts/{id} to get a specific post by ID. In contrast, a GraphQL API allows clients to request only the data they need, such as requesting a specific post with its title and author.

Authentication and authorization are essential, as seen in APIs like the GitHub API, which uses OAuth tokens for authentication. Proper documentation, like that provided by Stripe for their payment processing API, helps developers understand how to use the API effectively. Testing, as done by Netflix with their Simian Army for testing resilience, ensures that the API works reliably in different conditions.

Proper documentation, like that provided by Stripe for their payment processing API, helps developers understand how to use the API effectively. Testing, as done by Netflix with their Simian Army for testing resilience, ensures that the API works reliably in different conditions.

AI/ML Development

AI (Artificial Intelligence) and ML (Machine Learning) development involve creating algorithms and models that enable computers to perform tasks typically requiring human intelligence. This process starts with defining a specific problem or task that AI/ML can solve or improve.

Data collection and preparation are crucial, involving gathering and preparing a high-quality dataset representative of the problem domain. Feature engineering comes next, where relevant features are selected, transformed, and extracted from the dataset to help the model learn patterns and make predictions. Model selection and training follow, where an appropriate machine learning model is chosen and trained on the dataset to learn the underlying patterns.

Evaluation is then conducted to assess the model's performance using metrics to ensure it meets desired criteria. Hyperparameter tuning may be performed to optimize the model's performance. Once trained, the model is deployed into a production environment to make predictions on new, unseen data. Continuous monitoring and maintenance are essential to keep the model effective over time, including retraining it as necessary.

Python

Python is widely preferred for AI/ML development due to its simplicity and readability, making it easier to write and understand complex algorithms. It offers several powerful libraries for AI/ML, including

• NumPy: For numerical computing, providing support for large, multi-dimensional arrays and matrices.
• Pandas: For data manipulation and analysis, offering data structures and functions to efficiently manipulate large datasets.
• Matplotlib and Seaborn: For data visualization, allowing users to create various types of plots and charts.
• Scikit-learn: For machine learning, offering a simple and efficient tool for data mining and data analysis.
• TensorFlow and PyTorch: For deep learning, providing high-level neural network APIs for building and training deep learning models.

R

R, on the other hand, is specifically designed for statistical analysis and data visualization, making it a popular choice among statisticians and data scientists. It offers several packages and libraries for AI/ML development, including

• dplyr and tidyr: For data manipulation and transformation, providing a set of functions for efficiently manipulating datasets.
• ggplot2: For data visualization, offering a powerful and flexible system for creating graphics.
• caret and mlr: For machine learning, providing a unified interface for training and evaluating machine learning models.
• TensorFlow and Keras: Also available in R, allowing users to build and train deep learning models.

Conclusion

Familiarizing yourself with the ten most prevalent types of software development is crucial for individuals within the industry. This knowledge not only enhances communication with developers but also provides a comprehensive understanding of their projects.

Furthermore, it proves valuable when selecting a software development company to collaborate with. While each type has its subsets, this list serves as a foundational guide, steering you toward a better grasp of the diverse landscape of software development.