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Algorithms are the backbone behind technology that have helped establish some of the world’s most famous companies. Software giants like Google, beverage giants Coca Cola and many other organizations utilize proprietary algorithms to improve their services and enhance customer experience. Algorithms are an inseparable part of the technology behind organization as they help improve security, product or service recommendations, and increase sales.
Knowing the benefits of algorithms is useful, but you might also be interested to know what makes them so advantageous. As such, you’re probably asking: “What is an algorithm?” Here’s the most common algorithm definition: an algorithm is a set of procedures and rules a computer follows to solve a problem.
In addition to the meaning of the word “algorithm,” this article will also cover the key types and characteristics of algorithms, as well as their applications.
Types of Algorithms and Design Techniques
One of the main reasons people rely on algorithms is that they offer a principled and structured means to represent a problem on a computer.
Recursive algorithms are critical for solving many problems. The core idea behind recursive algorithms is to use functions that call themselves on smaller chunks of the problem.
Divide and Conquer Algorithms
Divide and conquer algorithms are similar to recursive algorithms. They divide a large problem into smaller units. Algorithms solve each smaller component before combining them to tackle the original, large problem.
A greedy algorithm looks for solutions based on benefits. More specifically, it resolves problems in sections by determining how many benefits it can extract by analyzing a certain section. The more benefits it has, the more likely it is to solve a problem, hence the term greedy.
Dynamic Programming Algorithms
Dynamic programming algorithms follow a similar approach to recursive and divide and conquer algorithms. First, they break down a complex problem into smaller pieces. Next, it solves each smaller piece once and saves the solution for later use instead of computing it.
After dividing a problem, an algorithm may have trouble moving forward to find a solution. If that’s the case, a backtracking algorithm can return to parts of the problem it has already solved until it determines a way forward that can overcome the setback.
Brute Force Algorithms
Brute force algorithms try every possible solution until they determine the best one. Brute force algorithms are simpler, but the solution they find might not be as good or elegant as those found by the other types of algorithms.
Algorithm Analysis and Optimization
Digital transformation remains one of the biggest challenges for businesses in 2023. Algorithms can facilitate the transition through careful analysis and optimization.
The time complexity of an algorithm refers to how long you need to execute a certain algorithm. A number of factors determine time complexity, but the algorithm’s input length is the most important consideration.
Before you can run an algorithm, you need to make sure your device has enough memory. The amount of memory required for executing an algorithm is known as space complexity.
Solving a problem with an algorithm in C or any other programming language is about making compromises. In other words, the system often makes trade-offs between the time and space available.
For example, an algorithm can use less space, but this extends the time it takes to solve a problem. Alternatively, it can take up a lot of space to address an issue faster.
Algorithms generally work great out of the box, but they sometimes fail to deliver the desired results. In these cases, you can implement a slew of optimization techniques to make them more effective.
You generally use memorization if you wish to elevate the efficacy of a recursive algorithm. The technique rewrites algorithms and stores them in arrays. The main reason memorization is so powerful is that it eliminates the need to calculate results multiple times.
As the name suggests, parallelization is the ability of algorithms to perform operations simultaneously. This accelerates task completion and is normally utilized when you have a lot of memory on your device.
Heuristic algorithms (a.k.a. heuristics) are algorithms used to speed up problem-solving. They generally target non-deterministic polynomial-time (NP) problems.
Another way to solve a problem if you’re short on time is to incorporate an approximation algorithm. Rather than provide a 100% optimal solution and risk taking longer, you use this algorithm to get approximate solutions. From there, you can calculate how far away they are from the optimal solution.
Algorithms sometimes analyze unnecessary data, slowing down your task completion. A great way to expedite the process is to utilize pruning. This compression method removes unwanted information by shrinking algorithm decision trees.
Algorithm Applications and Challenges
Thanks to this introduction to algorithm, you’ll no longer wonder: “What is an algorithm, and what are the different types?” Now it’s time to go through the most significant applications and challenges of algorithms.
Sorting algorithms arrange elements in a series to help solve complex issues faster. There are different types of sorting, including linear, insertion, and bubble sorting. They’re generally used for exploring databases and virtual search spaces.
An algorithm in C or other programming languages can be used as a searching algorithm. They allow you to identify a small item in a large group of related elements.
Graph algorithms are just as practical, if not more practical, than other types. Graphs consist of nodes and edges, where each edge connects two nodes.
There are numerous real-life applications of graph algorithms. For instance, you might have wondered how engineers solve problems regarding wireless networks or city traffic. The answer lies in using graph algorithms.
The same goes for social media sites, such as Facebook. Algorithms on such platforms contain nodes, which represent key information, like names and genders and edges that represent the relationships or dependencies between them.
When creating an account on some websites, the platform can generate a random password for you. It’s usually stronger than custom-made codes, thanks to cryptography algorithms. They can scramble digital text and turn it into an unreadable string. Many organizations use this method to protect their data and prevent unauthorized access.
Machine Learning Algorithms
Over 70% of enterprises prioritize machine learning applications. To implement their ideas, they rely on machine learning algorithms. They’re particularly useful for financial institutions because they can predict future trends.
Famous Algorithm Challenges
Many organizations struggle to adopt algorithms, be it an algorithm in data structure or computer science. The reason being, algorithms present several challenges:
- Opacity – You can’t take a closer look at the inside of an algorithm. Only the end result is visible, which is why it’s difficult to understand an algorithm.
- Heterogeneity – Most algorithms are heterogeneous, behaving differently from one another. This makes them even more complex.
- Dependency – Each algorithm comes with the abovementioned time and space restrictions.
Algorithm Ethics, Fairness, and Social Impact
When discussing critical characteristics of algorithms, it’s important to highlight the main concerns surrounding this technology.
Bias in Algorithms
Algorithms aren’t intrinsically biased unless the developer injects their personal biases into the design. If so, getting impartial results from an algorithm is highly unlikely.
Transparency and Explainability
Knowing only the consequences of algorithms prevents us from explaining them in detail. A transparent algorithm enables a user to view and understand its different operations. In contrast, explainability of an algorithm relates to its ability to provide reasons for the decisions it makes.
Privacy and Security
Some algorithms require end users to share private information. If cyber criminals hack the system, they can easily steal the data.
Algorithm Accessibility and Inclusivity
Limited explainability hinders access to algorithms. Likewise, it’s hard to include different viewpoints and characteristics in an algorithm, especially if it is biased.
Algorithm Trust and Confidence
No algorithm is omnipotent. Claiming otherwise makes it untrustworthy – the best way to prevent this is for the algorithm to state its limitations.
Algorithm Social Impact
Algorithms impact almost every area of life including politics, economic and healthcare decisions, marketing, transportation, social media and Internet, and society and culture in general.
Algorithm Sustainability and Environmental Impact
Contrary to popular belief, algorithms aren’t very sustainable. The extraction of materials to make computers that power algorithms is a major polluter.
Future of Algorithms
Algorithms are already advanced, but what does the future hold for this technology? Here are a few potential applications and types of future algorithms:
- Quantum Algorithms – Quantum algorithms are expected to run on quantum computers to achieve unprecedented speeds and efficiency.
- Artificial Intelligence and Machine Learning – AI and machine learning algorithms can help a computer develop human-like cognitive qualities via learning from its environment and experiences.
- Algorithmic Fairness and Ethics – Considering the aforementioned challenges of algorithms, developers are expected to improve the technology. It may become more ethical with fewer privacy violations and accessibility issues.
Smart, Ethical Implementation Is the Difference-Maker
Understanding algorithms is crucial if you want to implement them correctly and ethically. They’re powerful, but can also have unpleasant consequences if you’re not careful during the development stage. Responsible use is paramount because it can improve many areas, including healthcare, economics, social media, and communication.
If you wish to learn more about algorithms, accredited courses might be your best option. AI and machine learning-based modules cover some of the most widely-used algorithms to help expand your knowledge about this topic.
According to Statista, the U.S. cloud computing industry generated about $206 billion in revenue in 2022. Expand that globally, and the industry has a value of $483.98 billion. Growth is on the horizon, too, with Grand View Research stating that the various types of cloud computing will achieve a compound annual growth rate (CAGR) of 14.1% between 2023 and 2030.
The simple message is that cloud computing applications are big business.
But that won’t mean much to you if you don’t understand the basics of cloud computing infrastructure and how it all works. This article digs into the cloud computing basics so you can better understand what it means to deliver services via the cloud.
The Cloud Computing Definition
Let’s answer the key question immediately – what is cloud computing?
Microsoft defines cloud computing as the delivery of any form of computing services, such as storage or software, over the internet. Taking software as an example, cloud computing allows you to use a company’s software online rather than having to buy it as a standalone package that you install locally on your computer.
For the super dry definition, cloud computing is a model of computing that provides shared computer processing resources and data to computers and other devices on demand over the internet.
Cloud Computing Meaning
Though the cloud computing basics are pretty easy to grasp – you get services over the internet – what it means in a practical context is less clear.
In the past, businesses and individuals needed to buy and install software locally on their computers or servers. This is the typical ownership model. You hand over your money for a physical product, which you can use as you see fit.
You don’t purchase a physical product when using software via the cloud. You also don’t install that product, whatever it may be, physically on your computer. Instead, you receive the services managed directly by the provider, be they storage, software, analytics, or networking, over the internet. You (and your team) usually install a client that connects to the vendor’s servers, which contain all the necessary computational, processing, and storage power.
What Is Cloud Computing With Examples?
Perhaps a better way to understand the concept is with some cloud computing examples. These should give you an idea of what cloud computing looks like in practice:
- Google Drive – By integrating the Google Docs suite and its collaborative tools, Google Drive lets you create, save, edit, and share files remotely via the internet.
- Dropbox – The biggest name in cloud storage offers a pay-as-you-use service that enables you to increase your available storage space (or decrease it) depending on your needs.
- Amazon Web Services (AWS) – Built specifically for coders and programmers, AWS offers access to off-site remote servers.
- Microsoft Azure – Microsoft markets Azure as the only “consistent hybrid cloud.” This means Azure allows a company to digitize and modernize their existing infrastructure and make it available over the cloud.
- IBM Cloud – This service incorporates over 170 services, ranging from simple databases to the cloud servers needed to run AI programs.
- Salesforce – As the biggest name in the customer relationship management space, Salesforce is one of the biggest cloud computing companies. At the most basic level, it lets you maintain databases filled with details about your customers.
Common Cloud Computing Applications
Knowing what cloud computing is won’t help you much if you don’t understand its use cases. Here are a few ways you could use the cloud to enhance your work or personal life:
- Host websites without needing to keep on-site servers.
- Store files and data remotely, as you would with Dropbox or Salesforce. Most of these providers also provide backup services for disaster recovery.
- Recover lost data with off-site storage facilities that update themselves in real-time.
- Manage a product’s entire development cycle across one workflow, leading to easier bug tracking and fixing alongside quality assurance testing.
- Collaborate easily using platforms like Google Drive and Dropbox, which allow workers to combine forces on projects as long as they maintain an internet connection.
- Stream media, especially high-definition video, with cloud setups that provide the resources that an individual may not have built into a single device.
The Basics of Cloud Computing
With the general introduction to cloud computing and its applications out of the way, let’s get down to the technical side. The basics of cloud computing are split into five categories:
The interesting thing about cloud infrastructure is that it simulates a physical build. You’re still using the same hardware and applications. Servers are in play, as is networking. But you don’t have the physical hardware at your location because it’s all off-site and stored, maintained, and updated by the cloud provider. You get access to the hardware, and the services it provides, via your internet connection.
So, you have no physical hardware to worry about besides the device you’ll use to access the cloud service.
Off-site servers handle storage, database management, and more. You’ll also have middleware in play, facilitating communication between your device and the cloud provider’s servers. That middleware checks your internet connection and access rights. Think of it like a bridge that connects seemingly disparate pieces of software so they can function seamlessly on a system.
Cloud services are split into three categories:
Infrastructure as a Service (IaaS)
In a traditional IT setup, you have computers, servers, data centers, and networking hardware all combined to keep the front-end systems (i.e., your computers) running. Buying and maintaining that hardware is a huge cost burden for a business.
IaaS offers access to IT infrastructure, with scalability being a critical component, without forcing an IT department to invest in costly hardware. Instead, you can access it all via an internet connection, allowing you to virtualize traditionally physical setups.
Platform as a Service (PaaS)
Imagine having access to an entire IT infrastructure without worrying about all the little tasks that come with it, such as maintenance and software patching. After all, those small tasks build up, which is why the average small business spends an average of 6.9% of its revenue on dealing with IT systems each year.
PaaS reduces those costs significantly by giving you access to cloud services that manage maintenance and patching via the internet. On the simplest level, this may involve automating software updates so you don’t have to manually check when software is out of date.
Software as a Service (SaaS)
If you have a rudimentary understanding of cloud computing, the SaaS model is the one you are likely to understand the most. A cloud provider builds software and makes it available over the internet, with the user paying for access to that software in the form of a subscription. As long as you keep paying your monthly dues, you get access to the software and any updates or patches the service provider implements.
It’s with SaaS that we see the most obvious evolution of the traditional IT model. In the past, you’d pay a one-time fee to buy a piece of software off the shelf, which you then install and maintain yourself. SaaS gives you constant access to the software, its updates, and any new versions as long as you keep paying your subscription. Compare the standalone versions of Microsoft Office with Microsoft Office 365, especially in their range of options, tools, and overall costs.
Benefits of Cloud Computing
The traditional model of buying a thing and owning it worked for years. So, you may wonder why cloud computing services have overtaken traditional models, particularly on the software side of things. The reason is that cloud computing offers several advantages over the old ways of doing things:
- Cost savings – Cloud models allow companies to spread their spending over the course of a year. It’s the difference between spending $100 on a piece of software versus spending $10 per month to access it. Sure, the one-off fee ends up being less, but paying $10 per month doesn’t sting your bank balance as much.
- Scalability – Linking directly to cost savings, you don’t need to buy every element of a software to access the features you need when using cloud services. You pay for what you use and increase the money you spend as your business scales and you need deeper access.
- Mobility – Cloud computing allows you to access documents and services anywhere. Where before, you were tied to your computer desk if you wanted to check or edit a document, you can now access that document on almost any device.
- Flexibility – Tied closely to mobility, the flexibility that comes from cloud computing is great for users. Employees can head out into the field, access the services they need to serve customers, and send information back to in-house workers or a customer relationship management (CRM) system.
- Reliability – Owning physical hardware means having to deal with the many problems that can affect that hardware. Malfunctions, viruses, and human error can all compromise a network. Cloud service providers offer reliability based on in-depth expertise and more resources dedicated to their hardware setups.
- Security – The done-for-you aspect of cloud computing, particularly concerning maintenance and updates, means one less thing for a business to worry about. It also absorbs some of the costs of hardware and IT maintenance personnel.
Types of Cloud Computing
The types of cloud computing are as follows:
- Public Cloud – The cloud provider manages all hardware and software related to the service it provides to users.
- Private Cloud – An organization develops its suite of services, all managed via the cloud but only accessible to group members.
- Hybrid Cloud – Combines a public cloud with on-premises infrastructure, allowing applications to move between each.
- Community Cloud – While the community cloud has many similarities to a public cloud, it’s restricted to only servicing a limited number of users. For example, a banking service may only get offered to the banking community.
Challenges of Cloud Computing
Many a detractor of cloud computing notes that it isn’t as issue-proof as it may seem. The challenges of cloud computing may outweigh its benefits for some:
- Security issues related to cloud computing include data privacy, with cloud providers obtaining access to any sensitive information you store on their servers.
- As more services switch over to the cloud, managing the costs related to every subscription you have can feel like trying to navigate a spider’s web of software.
- Just because you’re using a cloud-based service, that doesn’t mean said service handles compliance for you.
- If you don’t perfectly follow a vendor’s terms of service, they can restrict your access to their cloud services remotely. You don’t own anything.
- You can’t do anything if a service provider’s servers go down. You have to wait for them to fix the issue, leaving you stuck without access to the software for which you’re paying.
- You can’t call a third party to resolve an issue your systems encounter with the cloud service because the provider is the only one responsible for their product.
- Changing cloud providers and migrating data can be challenging, so even if one provider doesn’t work well, companies may hesitate to look for other options due to sunk costs.
Cloud Computing Is the Present and Future
For all of the challenges inherent in the cloud computing model, it’s clear that it isn’t going anywhere. Techjury tells us that about 57% of companies moved, or were in the process of moving, their workloads to cloud services in 2022.
That number will only increase as cloud computing grows and develops.
So, let’s leave you with a short note on cloud computing. It’s the latest step in the constant evolution of how tech companies offer their services to users. Questions of ownership aside, it’s a model that students, entrepreneurs, and everyday people must understand.
Large portions of modern life revolve around computers. Many of us start the day by booting a PC and we spend the rest of our time carrying miniaturized computer devices around – our smartphones.
Such devices rely on complex software environments and programs to meet our personal and professional needs. And computer science deals with precisely that.
The job of a computer scientist revolves around software, including theoretical advances, software model design, and the development of new apps. It’s a profession that requires profound knowledge of algorithms, AI, cybersecurity, mathematical analysis, databases, and much more.
In essence, computer science is in the background of everything related to modern digital technologies. Computer scientists solve problems and advance the capabilities of technologies that nearly all industries utilize.
In fact, this scientific field is so broad that explaining what is computer science requires more than a mere definition. That’s why this article will go into considerable detail on the subject to flesh out the meaning behind one of the most important professions of our time.
History of Computer Science
The early history of computer science is a fascinating subject. On the one hand, the mechanics and mathematics that would form the core disciplines of computer science far predate the digital age. On the other hand, the modern iteration of computer science didn’t start until about two decades after the first digital computer came into being.
When examining the roots of computer science, we can go as far back as the antiquity era. Mechanical calculation tools and advanced mathematical algorithms date back millennia. However, those roots are too loosely connected to computer science.
The first people who started exploring the foundations of what is computer science today were Wilhelm Schickard and Gottfried Leibniz in early and late 17th century, respectively.
Schickard is responsible for the design of the world’s first genuine mechanical calculator. Leibniz is the inventor of a calculator that worked in the binary system, the universally known “1-0” number system that paved the way for the digital age.
Despite the early advances in the mentioned fields, it would be another 150 years after Leibniz before mechanical and automated computing machines saw industrial production. Yet, those machines weren’t used for any other purpose apart from calculations.
Computers became more powerful only in the 20th century. Like many other technologies, this branch saw rapid development during the last one hundred years, with IBM creating the first computing lab in 1945.
Yet, while plenty of research was happening, computer science wasn’t established as an independent discipline. That would take place only during the 1960s.
As mentioned, the invention of the binary system could be considered a root of computer science. This isn’t only due to the revolutionary mathematical model – it’s also because the binary number system lends itself particularly well to electronics.
The rise of electrical engineering moved forward inventions like the electrical circuit, the transistor, and powerful data storage solutions. This progress gave birth to the earliest electrical computers, which mostly found use in data processing.
It didn’t take long for massive companies to start using the early computers for information storage. Naturally, this use made further development of the technology necessary. The 1930s saw crucial milestones in computer theory, including the groundbreaking computational model by Alan Turing.
Not long after Turing, John von Neumann created a model of a computer that can store programs. By the 1950s, computers were in use in complex calculations and data processing on a large scale.
The rising demand made the binary machine language too unreliable and impractical. The successor, the so-called assembly language, soon proved just as lacking. By the end of the decade, the world saw the first program languages, which soon became the famed FORTRAN (Formula Translation) and COBOL (Common Business Oriented Language).
The following decade, it became obvious that computer science is a field of study in itself, rather than a subset of mathematical or physical disciplines.
Evolution of Computer Science Over Time
As technology kept progressing, computer science needed to keep up. The first computer operating systems came about in the 1960s, while the next two decades brought about an intense expansion in graphics and affordable hardware.
The combination of these factors (OS, accessible hardware, and graphical development) led to advanced user interfaces, championed by industry giants like Apple and Microsoft.
In parallel to these discoveries, computer networks were advancing, too. The birth of the internet added even more moving parts to the already vast field of computer science, including the first search engines that utilized advanced algorithms, albeit not at the same level as today’s engines.
Furthermore, greater computational capabilities created a need for better storage systems. This included larger databases and faster processing.
Today, computer science explores all of the mentioned facets of computer technology, alongside other fields like robotics and artificial intelligence.
Key Areas of Study in Computer Science
As you’ve undoubtedly noticed, computer science grew in scope with the development of computational technologies. That’s why it’s no surprise that computer science today encompasses many areas that deal with every aspect of the technology currently imaginable.
To answer the question of what is computer science, we’ll list some of the key areas of this discipline:
- Algorithms and data structures
- Programming languages and compilers
- Computer architecture and organization
- Operating systems
- Networking and communication
- Databases and information retrieval
- Artificial intelligence and machine learning
- Human-computer interaction
- Software engineering
- Computer graphics and visualization
As is apparent, these areas correspond with the historical advances in computational technology. We’ve talked about how algorithms predate the modern age by quite a lot. These mathematical achievements brought about early machine languages, which turned into programming languages.
The progress in data storage and the increased scope of the machines resulted in a need for more robust architecture, which necessitated the creation of operating systems. As computer systems started communicating with each other, better networking became vital.
Work on information retrieval and database management resulted from both individual computer use and a greater reliance on networking. Naturally, it didn’t take long for scientists to start considering how the machines could do even more work individually, which marked the starting point for modern AI.
Throughout its history, computer science developed new disciplines out of the need to solve existing problems and come up with novel solutions. When we consider all that progress, it’s clear that the practical applications of computer science grew alongside the technology itself.
Applications of Computer Science
Computer science is applied in numerous fields and industries. Currently, computer science contributes to the world through innovation and technological development. And as computer systems become more advanced, they are capable of resolving complex issues within some of the most important industries of our age.
Technology and Innovation
In terms of technology and innovation, computer science finds application in the fields of graphics, visualization, sound and video processing, mathematical modeling, analytics, and more.
Graphical rendering helps us visualize concepts that would otherwise be hard to grasp. Technologies like VR and AR expand the way we communicate, while 3D models flesh out future projects in staggering detail.
Sound and video processing capabilities of modern systems continue to revolutionize telecommunications. And, of course, mathematical modeling and analytics expand the possibilities of various systems, from physics to finance.
Problem-Solving in Various Industries
When it comes to the application of computer science in particular industries, this field of study contributes to better quality of life by tackling the most challenging problems in key areas:
Granted, these aren’t the only areas where computer science helps overcome issues and previous limitations.
In healthcare, computer systems can produce and analyze medical images, assisting medical experts in diagnosis and patient treatment. Furthermore, branches of computer science like psychoinformatics use digital technologies for a better understanding of psychological traits.
In terms of finance, data gathering and processing is critical for massive financial systems. Additionally, automation and networking make transactions easier and safer.
When it comes to education and entertainment, computer science offers solutions in terms of more comprehensible presentation, as well as more immersive experiences. Many schools worldwide use digital teaching tools today, helping students grasp complex subjects with fewer obstacles compared to traditional methods.
Careers in Computer Science
As should be expected, computer science provides numerous job opportunities in the modern market. Some of the most prominent roles in computer science include systems analysts, programmers, computer research scientists, database administrators, software developers, support specialists, cybersecurity specialists, and network administrators.
The mentioned roles require a level of proficiency in the appropriate field of computer science. Luckily, computer science skills are easier to learn today – mostly thanks to the development of computer science.
An online BSc or MSc in computer science can be an excellent way to get prepared for a career in the most sought-after profession in the modern world.
On that note, not all computer science jobs are projected to grow at the same rate by the end of this decade. Profiles that will likely stay in high demand include:
- Security Analyst
- Software Developer
- Research Scientist
- Database Administrator
Start Learning About Computer Science
Computer science represents a fascinating field that grows with the technology and, in some sense, fuels its own development. This vital branch of science has roots in ancient mathematical principles as well as the latest advances like machine learning and AI.
There are few fields worth exploring more today than computer science. Besides understanding our world better, learning more about computer science can open up incredible career paths and provide an opportunity to contribute to resolving some of the burning issues of our time.
When you decided to study for a BSc in Computer Science, you put your technical hat on. With reams of coding to wrap your head around (alongside a lot of technical talk about hardware), you’ve set yourself up for a career that could cover everything from software engineering and web development to data analysis.
But there’s another possibility that you may not have considered – engineering. Here, we answer the question “Can I do engineering after BSc Computer Science” and show you why the engineering path may be the right one to follow (both due to interest and potential career payout).
Options for Pursuing Engineering After BSc Computer Science
You have three options for pursuing engineering once you’re in possession of your BSc in Computer Science, some of which give you indirect entry into the field whereas others offer more practical or specialized education.
Lateral Entry into Engineering Courses
Your first choice is a course that combined the best of both worlds – a Bachelor of Engineering (Computer Science), otherwise known as B.E. Computer Science. As another full-time course, this program is usually spread over four years (though some institutions can fast-track you through a two-year course).
Strong high school scores in physics, math, and chemistry are a must if you decide to go down this route, with a minimum of 75% scored across all (with strong proficiency in English to boot). Assuming you hit those criteria, many colleges ask students to complete the Joint Entrance Exam (JEE), which is an exam that assesses your technical abilities and how you can apply those abilities to practical problems.
Master’s Degree in Engineering
Rather than going back to the bachelor’s level to study engineering after finishing your BSc in Computer Science (which is a lateral step as described above), you could keep marching forward. A Master’s degree in engineering is a post-graduate qualification, with most courses requiring you to have a Bachelor’s degree in a suitable technical subject. Engineering is the most obvious choice, though many Master’s programs accept students with computing backgrounds due to the technical nature of their knowledge.
Often called a “terminal” degree, meaning there are no doctorates for the engineering field, a Master’s in engineering should leave you with full accreditation so you can begin a career as a chartered engineer. Thankfully, you don’t usually have to rely on an entrance exam to start the course, as long as you have an appropriate Bachelor’s degree.
Specialized Engineering Courses and Certifications
There’s plenty of crossover between the engineering and computer science paths, particularly when it comes to devising solutions for physical hardware:
- Network Engineering – Designed to equip you with advanced skills in computing (especially in the areas of developing and managing network systems), network engineering courses come in several flavors. Some universities offer them as specialized Master’s programs, assuming you have an appropriate technical Bachelor’s degree. In some cases, you can enter into trainee courses with workplaces that equip you with network engineering skills, with this option sometimes not requiring formal computer science training beforehand.
- Cyber Security Engineering – With cybercrime losses exceeding $10 billion in 2022 (according to the FBI), there’s an obvious demand for people who can engineer systems designed to deter hackers. Specialized programs, such as an MSc in cyber security engineering, equip you with the ability to offer hardware security services and reverse-engineer cyber-attacks. Entry requirements vary depending on your university, though many ask for a minimum second-class degree in a subject like computer science or electronic engineering.
- Applied Data Science – You’ll pick up on some of the technical concepts that underpin data science while studying for your BSc in Computer Science. A Master’s degree in applied data science teaches you the practical side, equipping you with the skills you need to analyze and work on complicated engineering assets. Again, a degree in a technical subject (like computer science) should be enough for most universities, with this course also offering a path into Ph.D. studies in the applied data science and data-based industrial engineering areas.
Benefits of Pursuing Engineering After BSc Computer Science
After having worked so hard to obtain your BSc in Computer Science, the question “can I do engineering after BSc Computer Science?” may not have crossed your mind. After all, you’re equipped to enter the workforce already, so you’re wondering what the benefits of further study may be. Here are three to consider.
Enhanced Career Prospects
Having a joint specialization between engineering and computer science can be your pathway to a higher salary, with specific specializations in applied data science or cyber security engineering veering into six-figure territory.
According to Glass Door, starting salaries for applied data scientists start at around $83,000, though the average is $126,586 per year. Advance in that path until you become a senior or lead data scientist and you’ll find your earnings in the $160,000 range. The same resource suggests the average base pay for a cyber security engineer is nearly as impressive, starting at $92,297 per year, though some organizations offer six-figure contracts for those who have some experience under their belts.
Specialization in a Specific Field
Though a BSc in Computer Science equips you with a ton of foundational knowledge, it can leave you feeling unfocused as potential career paths branch out in front of you. Rather than exploring every one of those branches, shifting into engineering allows you to distill (and build upon) what you already know to create a more focused knowledge base.
In addition to making you more desirable to potential employers (as we see above), a specialization makes it easier to find a job that fits your skill set. You add a layer of polish to your raw skillset, developing an understanding of where your specific talents lie and, more importantly, how you can apply them.
Opportunities for Research and Innovation
Having the skills to access better careers is one thing, but being able to contribute to the development of new technologies can make you feel like you’re making a real difference to the world. Following up your BSc in Computer Science with an engineering specialization equips you with practical knowledge (complementing your technical prowess) to give you the perfect balance for entering into the research world.
As one example, Imperial College London operates a research program that takes a data-driven approach to data science research. Applications of the tech (and ideas) that come from that program are used in fields as diverse as medicine, astrophysics, and finance, allowing researchers to create cross-industry change while working with cutting-edge tech.
Steps to Pursue an Engineering Career Post-BSc
Now that you know that the answer to “Can I do engineering after BSc Computer Science?” is a definite “yes,” there’s one more question to answer:
Step 1 – Research and Choose the Right Engineering Program
Choosing the right engineering program may make you feel like you’re at the starting point of a path that branches out in a dozen directions. Each of those paths has something to offer, though you have to commit to one to become a specialist. Think about what you enjoyed while studying computer science, which, combined with an understanding of your career goals, will help you determine which path leads you toward your passion.
Once you know what you want to study (and why), evaluate the programs open to you using the curriculum offered and the reputations of the programs as your criteria for making a choice.
Step 2 – Prepare for Entrance Exams and Application Process
You’re not going to simply walk into an engineering course because you have a BSc in Computer Science, even if your graduate studies equip you with most of the skills necessary to start a post-graduate engineering course. Some institutions have entrance exams (with the previously mentioned JEE being popular), meaning you need to gather study materials and focus your efforts on passing that exam.
For universities that are happy to accept your BSc in Computer Science as proof of your ability, you still need to complete applications and file them before the appropriate deadlines. These deadlines vary depending on where you apply. For instance, you usually have until the end of June if applying for a program that accepts fall admissions in the United States.
Step 3 – Gain Relevant Work Experience
The more work experience you can get under your belt, especially when studying, the better your resume will look when you start applying for specialized computer engineering roles. Internships and co-op programs can equip you with practical knowledge of the workforce (and help you to build connections), though they’re often unpaid.
If working without pay is a problem for you, accepting part-time or freelance work in an engineering field related to your specialization is an option. Just be wary of burnout if you’re still in the process of completing your studies.
Step 4 – Network With Professionals in the Engineering Field
There’s an old saying that goes “It’s not what you know, it’s who you know.” While that isn’t always the case in engineering (merit and skills go a long way), it still helps to have connections in the field who can point you in the direction of roles and employers.
Attending industry events and conferences (even if you’re not actively looking for a job yet) allows you to hobnob with people who may prove useful when you’re trying to break into the engineering sector. Joining professional associations, such as the Association for Computing Machinery (ACM), offers resources, continuing education, and access to career centers that can help you to get ahead.
Engineer Your Path to a New Career
Computer science and engineering make for good bedfellows, with both fields being highly technical and reliant on you having strong mathematical skills. Perhaps that’s why there are so many attractive (and potentially lucrative) options for specializations, with each offering ways to apply the foundational knowledge you develop during a BSc in Computer Science.
When making your choice, start by figuring out which field grabs your interest before taking the steps described above to reach your career goals.
With your BSc in Computer Science achieved, you have a ton of technical knowledge in coding, systems architecture, and the general “whys” and “hows” of computing under your belt. Now, you face a dilemma, as you’re entering a field that over 150,000 people study for per year, meaning competition is rife.
That huge level of competition makes finding a new career difficult, as UK-based computer science graduates discovered in the mid-2010s when the saturation of the market led to an 11% unemployment rate. To counter that saturation, you may find the siren’s call of the business world tempts you toward continuing your studies to obtain an MBA.
So, the question is – can I do MBA after Computer Science?
This article offers the answers.
Understanding the MBA Degree
MBAs exist to equip students with the knowledge (both technical and practical) to succeed in the business world. For computer science graduates, that may mean giving them the networking and soft skills they need to turn their technical knowledge into career goldmines, or it could mean helping them to start their own companies in the computing field.
Most MBAs feature six core subjects:
- Finance – Focused on the numbers behind a business, this subject is all about learning how to balance profits, losses, and the general costs of running a business.
- Accounting – Building on the finance subject, accounting pulls students into the weeds when it comes to taxes, operating expenses, and running a healthy company.
- Leadership – Soft skills are just as important as hard skills to a business student, with leadership subjects focusing on how to inspire employees and foster teamwork.
- Economic Statistics – The subject that most closely relates to a computer science degree, economic statistics is all about processing, collecting, and interpreting technical data.
- Accountability/Ethics – With so many fields having strict compliance criteria (coupled with the ethical conundrums that arise in any business), this subject helps students navigate potential legal and ethical minefields.
- Marketing – Having a great product or service doesn’t always lead to business success. Marketing covers what you do to get what you have to offer into the public eye.
Beyond the six core subjects, many MBAs offer students an opportunity to specialize via additional courses in the areas that interest them most. For instance, you could take courses in entrepreneurship to bolster your leadership skills and ethical knowledge, or focus on accounting if you’re more interested in the behind-the-scenes workings of the business world.
As for career opportunities, you have a ton of paths you can follow (with your computer science degree offering more specialized career routes). Those with an MBA alone have options in the finance, executive management, and consulting fields, with more specialized roles in IT management available to those with computer science backgrounds.
Eligibility for MBA After BSc Computer Science
MBAs are attractive to prospective post-graduate students because they have fairly loose requirements, at least when compared to more specialized further studies. Most MBA courses require the following before they’ll accept a student:
- A Bachelor’s degree in any subject, as long as that degree comes from a recognized educational institution
- English language proficiency
- This is often tested using either the TOEFL or IELTS tests
- A pair of recommendation letters, which can come from employers or past teachers
- Your statement of purpose defining why you want to study for an MBA
- A resume
- A Graduate Management Admissions Test (GMAT) score
- You’ll receive a score between 200 and 800, with the aim being to exceed the average of 574.51
Interestingly, some universities offer MBAs in Computer Science, which are the ideal transitional courses for those who are wary of making the jump from a more technical field into something business-focused. Course requirements are similar to those for a standard MBA, though some universities also like to see that you have a couple of years of work experience before you apply.
Benefits of Pursuing an MBA After BSc Computer Science
So, the answer to “Can I do MBA after BSc Computer Science,” is a resounding “yes,” but we still haven’t confronted why that’s a good choice. Here are five reasons:
- Diversify your skill set – While your skill set after completing a computer science degree is extremely technical, you may not have many of the soft skills needed to operate in a business environment. Beyond teaching leadership, management, and teamwork, a good MBA program also helps you get to grips with the numbers behind a business.
- Expand career opportunities – There is no shortage of potential roles for computer science graduates, though the previously mentioned study data shows there are many thousands of people studying the same subject. With an MBA to complement your knowledge of computers, you open the door to career opportunities in management fields that would otherwise not be open to you.
- Enhance leadership and management skills – Computer science can often feel like a solitary pursuit, as you spend more time behind a keyboard than you do interacting with others. MBAs are great for those who need a helping hand with their communication skills. Plus, they’re ideal for teaching the organizational aspects of running (or managing) a business.
- Potential for higher salary and career growth – According to Indeed, the average salary in the computer science field is $103,719. Figures from Seattle University suggest those with MBAs can far exceed that average, with the figures it quotes from the industry journal Poets and Quants suggesting an average MBA salary of $140,924.
Challenges and Considerations
As loose as the academic requirements for being accepted to an MBA may be (at least compared to other subjects), there are still challenges to confront as a computer science graduate or student.
- The time and financial investments – Forbes reports the average cost of an MBA in the United States to be $61,800. When added to the cost of your BSc in Computer Science, it’s possible you’ll face near-six-figure debt upon graduating. Couple that monetary investment with the time taken to get your MBA (it’s a full-time course) and you may have to put more into your studies than you think.
- Balancing your technical and managerial skills – Computer science focuses on the technical side, which is only one part of an MBA. While the skills you have will come to the fore when you study accounting or economic statistics, the people-focused aspects of an MBA may be a challenge.
- Adjusting to a new academic environment – You’re switching focus from the computer screen to a more classroom-led learning environment. Some may find this a challenge, particularly if they appreciate the less social aspects of computer science.
MBA Over Science – The Thomas Henson Story
After completing his Bachelor’s degree in computer information systems, Thomas Henson faced a choice – start a Master’s degree in science or study for his MBA. Having worked as a software engineer for six months following his graduation, he wanted to act fast to get his Masters’s done and dusted, opening up new career opportunities in the process.
Eventually, he chose an MBA and now works as a senior software engineer specializing in the Hortonworks Data Platform. On his personal blog, he shares why he chose an MBA over a Master’s degree in computer science, with his insights possibly helping others make their own choice:
- Listen to the people around you (especially teachers and mentors) and ask them why they’ve chosen their career and study paths.
- Compare programs (both comparing MBAs against one another and comparing MBAs to other post-graduate degrees) to see which courses serve your future ambitions best.
- Follow your passion (James loved accounting) as the most important thing is not necessarily the post-graduate course you take. The most important thing is that you finish.
Choosing the Right MBA Program
Finding the right MBA program means taking several factors into consideration, with the following four being the most important:
- Reputation and accreditation – The reputation of the institution you choose, as well as the accreditation it holds, plays a huge role in your decision. Think of your MBA as a recommendation. That recommendation doesn’t mean much if it comes from a random person in the street (i.e., an institution nobody knows), but it carries a lot of weight if it comes from somebody respected.
- Curriculum and specialization – As Thomas Henson points out, what drives you most is what will lead you to the right MBA. In his case, he loved accounting enough to make an MBA a possibility, and likely pursued specializations in that area. Ask yourself what you specifically aim to achieve with your MBA and look for courses that move you closer to that goal.
- Networking opportunities – As anybody in the business world will tell you, who you know is often as important as what you know. Look for a course that features respected lecturers and professors, as they have connections that you can exploit, and take advantage of any opportunities to go to networking events or join professional associations.
- Financial aid and scholarships – Your access to financial aid depends on your current financial position, meaning it isn’t always available. Scholarships may be more accessible, with major institutions like Harvard and Columbia Business School offering pathways into their courses for those who meet their scholarship requirements.
Speaking of Harvard and Columbia, it’s also a good idea to research some of the top business schools, especially given that the reputation of your school is as important as the degree you earn. Major players, at least in the United States, include:
- Harvard Business School
- Columbia Business School
- Wharton School of Business
- Yale School of Management
- Stanford Graduate School of Business
Become a Business-Minded Computer Buff
With the technical skills you earned from your BSc in Computer Science, you’ll be happy to find that the answer to “Can I do MBA after BSc Computer Science?” is “Yes.” Furthermore, it’s recommended as an MBA can equip you with soft skills, such as communication and leadership, that you may not receive from your computing studies. Ultimately, the combination of tech-centric and business skills opens the door to new career paths, with the average earnings of an MBA student outclassing those of computer science graduates.
Your choice comes down to your passion and the career you wish to pursue. If management doesn’t appeal to you, an MBA is likely a waste of time (and over $60,000), whereas those who want to apply their tech skills to the business world will get a lot more out of an MBA.
With your BSc in Computer Science completed you have a ton of technical skills (ranging from coding to an in-depth understanding of computer architecture) to add to your resume. But post-graduate education looms and you’re tossing around various options, including doing an MCA (Master of computer applications).
An MCA builds on what you learned in your BSc, with fields of study including computational theory, algorithm design, and a host of mathematical subjects. Knowing that, you’re asking yourself “Can I do MCA after BSc Computer Science?” Let’s answer that question.
Eligibility for MCA After BSc Computer Science
The question of eligibility inevitably comes up when applying to study for an MCA, with three core areas you need to consider:
- The minimum requirements
- Entrance exams and admissions processes
- Your performance in your BSc in Computer Science
Starting with the basics, this is what you need to apply for to study for your MCA:
- A Bachelor’s degree in a relevant computing subject (like computer science or computer applications.)
- Some institutions accept equivalent courses and external courses as evidence of your understanding of computers
- If you’re an international student, you’ll likely need to pass an English proficiency test
- IELTS and TOEFL are the most popular of these tests, though some universities require a passing grade in a PTE test.
- Evidence that you have the necessary financial resources to cover the cost of your MCA
- Costs vary but can be as much as $40,000 for a one or two-year course.
Entrance Exams and Admission Processes
Some universities require you to take entrance exams, which can fall into the following categories:
- National Level – You may have to take a national-level exam (such as India’s NIMCET) to demonstrate your basic computing ability.
- State-Level – Most American universities don’t require state-level entrance exams, though some international universities do. For instance, India has several potential exams you may need to take, including the previously-mentioned NIMCET, the WBJECA, and the MAH MCA CET. All measure your computing competence, with most also requiring you to have completed your BSc in Computer Science before you can take the exam.
- University-Specific – Many colleges, at least in the United States, require students to have passing grades in either the ACT or SATs, both of which you take at the high school level. Some colleges have also started accepting the CLT, which is a new test that positions itself as an alternative to the ACT or SAT. The good news is that you’ll have taken these tests already (assuming you study in the U.S.), so you don’t have to take them again to study for your MCA.
Your Performance Matters
How well you do in your computer science degree matters, as universities have limited intakes and will always favor the highest-performing students (mitigating circumstances notwithstanding). For example, many Indian universities that offer MCAs ask students to achieve at least a 50% or 60% CGPA (Cumulative Grade Point Average) across all modules before considering the student for their programs.
Benefits of Pursuing MCA After BSc Computer Science
Now you know the answer to “Can I do MCA after BSc Computer Science,” is that you can (assuming you meet all other criteria), you’re likely asking yourself if it’s worth it. These three core benefits make pursuing an MCA a great use of your time:
- Enhanced Knowledge and Skills – If your BSc in Computer Science is like the foundation that you lay before building a house, an MCA is the house itself. You’ll be building up the basic skills you’ve developed, which includes getting to grips with more advanced programming languages and learning the intricacies of software development. Those who are more interested in the hardware side of things can dig into the specifics of networking.
- Improved Career Prospects – Your career prospects enjoy a decent bump if you have an MCA, with Pay Scale noting the average base salary of an MCA graduate in the United States to be $118,000 per year. That’s about $15,000 more per year than the $103,719 salary Indeed says a computer scientist earns. Add in the prospect of assuming higher (or more senior) roles in a company and the increased opportunities for specialization that come with post-graduate studies and your career prospects look good.
- Networking Opportunities – An MCA lets you delve deeper into the computing industry, exposing you to industry trends courtesy of working with people who are already embedded within the field. Your interactions with existing professionals work wonders for networking, giving you access to connections that could enhance your future career. Plus, you open the door to internships with more prestigious companies, in addition to participating in study projects that look attractive on a resume.
Career Prospects after MCA
After you’ve completed your MCA, the path ahead of you branches out, opening up the possibilities of entering the workforce or continuing your studies.
Job Roles and Positions
If you want to jump straight into the workforce once you have your MCA, there are several roles that will welcome you with open arms:
- Software Developer/Engineer – Equipped with the advanced programming skills an MCA provides, you’re in a great position to take a junior software development role that can quickly evolve into a senior position.
- Systems Analyst – Organization is the name of the game when you’re a systems analyst. These professionals focus on how existing computer systems are organized, coming up with ways to streamline IT operations to get companies operating more efficiently.
- Database Administrator – Almost any software (or website) you care to mention has databases running behind the scenes. Database administrators organize these virtual “filing systems,” which can cover everything from basic login details for websites to complex financial information for major companies.
- Network Engineer – Even the most basic office has a computer network (taking in desktops, laptops, printers, servers, and more) that requires management. A Network engineer provides that management, with a sprinkling of systems analysis that may help with the implementation of new networks.
- IT Consultant – If you don’t want to be tied down to one company, you can take your talents on the road to serve as an IT consultant for companies that don’t have in-house IT teams. You’ll be a “Jack of all trades” in this role, though many consultants choose to specialize in either the hardware or software sides.
Industries and Sectors
Moving away from specific roles, the skills you earn through an MCA makes you desirable in a host of industries and sectors:
- IT and Software Companies – The obvious choice for an MCA graduate, IT and software focus on hardware and software respectively. It’s here where you’ll find the software development and networking roles, though whether you work for an agency, as a solo consultant, or in-house for a business is up to you.
- Government Organizations – In addition to the standard software and networking needs that government agencies face (like most workplaces), cybersecurity is critical in this field. According to Security Intelligence, 106 government or state agencies faced ransomware attacks in 2022, marking nearly 30 more attacks than they faced the year prior. You may be able to turn your knowledge to thwarting this rising tide of cyber-threats, though there are many less security-focused roles available in government organizations.
- Educational Institutions – The very institutions from which you earn your MCA have need of the skills they teach. You’ll know this yourself from working first-hand with the complex networks of computing hardware the average university or school has. Throw software into the mix and your expertise can help educational institutions save money and provide better services to students.
- E-Commerce and Startups – Entrepreneurs with big ideas need technical people to help them build the foundations of their businesses, meaning MCAs are always in demand at startups. The same applies to e-commerce companies, which make heavy use of databases to store customer and financial details.
Further Education and Research Opportunities
You’ve already taken a big step into further education by completing an MCA (which is a post-graduate course), so you’re in the perfect place to take another step. Choosing to work on getting your doctorate in computer science requires a large time commitment, with most programs taking between four and five years, but it allows for more independent study and research. The financial benefits may also be attractive, with Salary.com pointing to an average base salary of $120,884 (before bonuses and benefits) for those who take their studies to the Ph.D. level.
Top MCA Colleges and Universities
Drawing from data provided by College Rank, the following are the top three colleges for those interested in an MCA:
- The University of Washington – A 2.5-year course that is based in the college’s Seattle campus, the University of Washington’s MCA is a part-time program that accepts about 60% of the 120 applicants it receives each year.
- University of California-Berkeley (UCB) – UCB’s program is a tough one to get into, with students needing to achieve a minimum 3.0 Grade Point Average (GPA) on top of having three letters of recommendation. But once you’re in, you’ll join a small group of students focused on research into AI, database management, and cybersecurity, among other areas.
- University of Illinois – Another course that has stringent entry requirements, the University of Illinois’s MCA program requires you to have a 3.2 GPA in your BSc studies to apply. It’s also great for those who wish to specialize, as you get a choice of 11 study areas to focus on for your thesis.
Pursuing an MCA after completing your BSc in Computer Science allows you to build up from your foundational knowledge. Your career prospects open up, meaning you’ll spend less time “working through the ranks” than you would if you enter the workforce without an MCA. Plus, the data shows that those with MCAs earn an average of about $15,000 per year more than those with a BSc in Computer Science.
If you’re pondering the question, “Can I do MCA after BSc Computer Science,” the answer comes down to what you hope to achieve in your career. Those interested in positions of seniority, higher pay scales, and the ability to specialize in specific research areas may find an MCA attractive.
In today’s digital landscape, few businesses can go without relying on cloud computing to build a rock-solid IT infrastructure. Boosted efficiency, reduced expenses, and increased scalability are just some of the reasons behind its increasing popularity.
In case you aren’t familiar with the concept, cloud computing refers to running software and services on the internet using data stored on outside sources. So, instead of owning and maintaining their infrastructure locally and physically, businesses access cloud-based services as needed.
And what is found in the cloud? Well, any crucial business data that you can imagine. Customer information, business applications, data backups, and the list can go on.
Given this data’s sensitivity, cloud computing security is of utmost importance.
Unfortunately, cloud computing isn’t the only aspect that keeps evolving. So do the risks, issues, and challenges threatening its security.
Let’s review the most significant security issues in cloud computing and discuss how to address them adequately.
Understanding Cloud Computing Security Risks
Cloud computing security risks refer to potential vulnerabilities in the system that malicious actors can exploit for their own benefit. Understanding these risks is crucial to selecting the right cloud computing services for your business or deciding if cloud computing is even the way to go.
A data breach happens when unauthorized individuals access, steal, or publish sensitive information (names, addresses, credit card information). Since these incidents usually occur without the organization’s knowledge, the attackers have ample time to do severe damage.
What do we mean by damage?
Well, in this case, damage can refer to various scenarios. Think everything from using the stolen data for financial fraud to sabotaging the company’s stock price. It all depends on the type of stolen data.
Whatever the case, companies rarely put data breaches behind them without a severely damaged reputation, significant financial loss, or extensive legal consequences.
The business world revolves around data. That’s why attackers target it. And why companies fight so hard to preserve it.
As the name implies, data loss occurs when a company can no longer access its previously stored information.
Sure, malicious attacks are often behind data loss. But this is only one of the causes of this unfortunate event.
The cloud service provider can also accidentally delete your vital data. Physical catastrophes (fires, floods, earthquakes, tornados, explosions) can also have this effect, as can data corruption, software failure, and many other mishaps.
Using (or reusing) weak passwords as part of cloud-based infrastructure is basically an open invitation for account hijacking.
Again, the name is pretty self-explanatory – a malicious actor gains complete control over your online accounts. From there, the hijacker can access sensitive data, perform unauthorized actions, and compromise other associated accounts.
In cloud computing, communication service providers (CSPs) offer their customers numerous Application Programming Interfaces (APIs). These easy-to-use interfaces allow customers to manage their cloud-based services. But besides being easy to use, some of these APIs can be equally easy to exploit. For this reason, cybercriminals often prey on insecure APIs as their access points for infiltrating the company’s cloud environment.
Denial of Service (DoS) Attacks
Denial of service (DoS) attacks have one goal – to render your network or server inaccessible. They do so by overwhelming them with traffic until they malfunction or crash.
It’s clear that these attacks can cause severe damage to any business. Now imagine what they can do to companies that rely on those online resources to store business-critical data.
Not all employees will have your company’s best interest at heart, not to mention ex-employees. If these individuals abuse their authorized access, they can wreak havoc on your networks, systems, and data.
Insider threats are more challenging to spot than external attacks. After all, these individuals know your business inside out, positioning them to cause serious damage while staying undetected.
Advanced Persistent Threats (APTs)
With advanced persistent threats (APTs), it’s all about the long game. The intruder will infiltrate your company’s cloud environment and fly under the radar for quite some time. Of course, they’ll use this time to steal sensitive data from your business’s every corner.
Challenges in Cloud Computing Security
Security challenges in cloud computing refer to hurdles your company might hit while implementing cloud computing security.
Shared Responsibility Model
A shared responsibility model is precisely what it sounds like. The responsibility for maintaining security falls on several individuals or entities. In cloud computing, these parties include the CSP and your business (as the CSP’s consumer). Even the slightest misunderstanding concerning the division of these responsibilities can have catastrophic consequences for cloud computing security.
Compliance With Regulations and Standards
Organizations must store their sensitive data according to specific regulations and standards. Some are industry-specific, like HIPAA (Health Insurance Portability and Accountability Act) for guarding healthcare records. Others, like GDPR (General Data Protection Regulation), are more extensive. Achieving this compliance in cloud computing is more challenging since organizations typically don’t control all the layers of their infrastructure.
Data Privacy and Protection
Placing sensitive data in the cloud comes with significant exposure risks (as numerous data breaches in massive companies have demonstrated). Keeping this data private and protected is one of the biggest security challenges in cloud computing.
Lack of Visibility and Control
Once companies move their data to the cloud (located outside their corporate network), they lose some control over it. The same goes for their visibility into their network’s operations. Naturally, since companies can’t fully see or control their cloud-based resources, they sometimes fail to protect them successfully against attacks.
Vendor Lock-In and Interoperability
These security challenges in cloud computing arise when organizations want to move their assets from one CSP to another. This move is often deemed too expensive or complex, forcing the organization to stay put (vendor lock-in). Migrating data between providers can also cause different applications and systems to stop working together correctly, thus hindering their interoperability.
Security of Third-Party Services
Third-party services are often trouble, and cloud computing is no different. These services might have security vulnerabilities allowing unauthorized access to your cloud data and systems.
Issues in Cloud Computing Security
The following factors have proven as major security issues in cloud computing.
Insufficient Identity and Access Management
The larger your business, the harder it gets to establish clearly-defined roles and assign them specific permissions. However, Identity and Access Management (IAM) is vital in cloud computing. Without a comprehensive IAM strategy, a data breach is just waiting to happen.
Inadequate Encryption and Key Management
Encryption is undoubtedly one of the most effective measures for data protection. But only if it’s implemented properly. Using weak keys or failing to rotate, store, and protect them adequately is a one-way ticket to system vulnerabilities.
So, without solid encryption and coherent key management strategies, your cloud computing security can be compromised in no time.
Vulnerabilities in Virtualization Technology
Virtualization (running multiple virtual computers on the hardware elements of a single physical computer) is becoming increasingly popular. Consider the level of flexibility it allows (and at what cost!), and you’ll understand why.
However, like any other technology, virtualization is prone to vulnerabilities. And, as we’ve already established, system vulnerabilities and cloud computing security can’t go hand in hand.
Limited Incident Response Capabilities
Promptly responding to a cloud computing security incident is crucial to minimizing its potential impact on your business. Without a proper incident report strategy, attackers can run rampant within your cloud environment.
Security Concerns in Multi-Tenancy Environments
In a multi-tenancy environment, multiple accounts share the same cloud infrastructure. This means that an attack on one of those accounts (or tenants) can compromise the cloud computing security for all the rest. Keep in mind that this only applies if the CSP doesn’t properly separate the tenants.
Addressing Key Concerns in Cloud Computing Security
Before moving your data to cloud-based services, you must fully comprehend all the security threats that might await. This way, you can implement targeted cloud computing security measures and increase your chances of emerging victorious from a cyberattack.
Here’s how you can address some of the most significant cloud computing security concerns:
- Implement strong authentication and access controls (introducing multifactor authentication, establishing resource access policies, monitoring user access rights).
- Ensure data encryption and secure key management (using strong keys, rotating them regularly, and protecting them beyond CSP’s measures).
- Regularly monitor and audit your cloud environments (combining CSP-provided monitoring information with your cloud-based and on-premises monitoring information for maximum security).
- Develop a comprehensive incident response plan (relying on the NIST [National Institute of Standards and Technology] or the SANS [SysAdmin, Audit, Network, and Security] framework).
- Collaborate with cloud service providers to successfully share security responsibilities (coordinating responses to threats and investigating potential threats).
Weathering the Storm in Cloud Computing
Due to the importance of the data they store, cloud-based systems are constantly exposed to security threats. Compare the sheer number of security risks to the number of challenges and issues in addressing them promptly, and you’ll understand why cloud computing security sometimes feels like an uphill battle.
Since these security threats are ever-evolving, staying vigilant, informed, and proactive is the only way to stay on top of your cloud computing security. Pursue education in this field, and you can achieve just that.
Did you know you’re participating in a distributed computing system simply by reading this article? That’s right, the massive network that is the internet is an example of distributed computing, as is every application that uses the world wide web.
Distributed computing involves getting multiple computing units to work together to solve a single problem or perform a single task. Distributing the workload across multiple interconnected units leads to the formation of a super-computer that has the resources to deal with virtually any challenge.
Without this approach, large-scale operations involving computers would be all but impossible. Sure, this has significant implications for scientific research and big data processing. But it also hits close to home for an average internet user. No distributed computing means no massively multiplayer online games, e-commerce websites, or social media networks.
With all this in mind, let’s look at this valuable system in more detail and discuss its advantages, disadvantages, and applications.
Basics of Distributed Computing
Distributed computing aims to make an entire computer network operate as a single unit. Read on to find out how this is possible.
Components of a Distributed System
A distributed system has three primary components: nodes, communication channels, and middleware.
The entire premise of distributed computing is breaking down one giant task into several smaller subtasks. And who deals with these subtasks? The answer is nodes. Each node (independent computing unit within a network) gets a subtask.
For nodes to work together, they must be able to communicate. That’s where communication channels come into play.
Middleware is the middleman between the underlying infrastructure of a distributed computing system and its applications. Both sides benefit from it, as it facilitates their communication and coordination.
Types of Distributed Systems
Coordinating the essential components of a distributed computing system in different ways results in different distributed system types.
A client-server system consists of two endpoints: clients and servers. Clients are there to make requests. Armed with all the necessary data, servers are the ones that respond to these requests.
The internet, as a whole, is a client-server system. If you’d like a more specific example, think of how streaming platforms (Netflix, Disney+, Max) operate.
Peer-to-peer systems take a more democratic approach than their client-server counterparts: they allocate equal responsibilities to each unit in the network. So, no unit holds all the power and each unit can act as a server or a client.
Content sharing through clients like BitTorrent, file streaming through apps like Popcorn Time, and blockchain networks like Bitcoin are some well-known examples of peer-to-peer systems.
Coordinate a grid of geographically distributed resources (computers, networks, servers, etc.) that work together to complete a common task, and you get grid computing.
Whether belonging to multiple organizations or far away from each other, nothing will stop these resources from acting as a uniform computing system.
In cloud computing, centralized data centers store data that organizations can access on demand. These centers might be centralized, but each has a different function. That’s where the distributed system in cloud computing comes into play.
Thanks to the role of distributed computing in cloud computing, there’s no limit to the number of resources that can be shared and accessed.
Key Concepts in Distributed Computing
For a distributed computing system to operate efficiently, it must have specific qualities.
If workload growth is an option, scalability is a necessity. Amp up the demand in a distributed computing system, and it responds by adding more nodes and consuming more resources.
In a distributed computing system, nodes must rely on each other to complete the task at hand. But what happens if there’s a faulty node? Will the entire system crash? Fortunately, it won’t, and it has fault tolerance to thank.
Instead of crashing, a distributed computing system responds to a faulty node by switching to its working copy and continuing to operate as if nothing happened.
A distributed computing system will go through many ups and downs. But through them all, it must uphold consistency across all nodes. Without consistency, a unified and up-to-date system is simply not possible.
Concurrency refers to the ability of a distributed computing system to execute numerous processes simultaneously.
Parallel computing and distributed computing have this quality in common, leading many to mix up these two models. But there’s a key difference between parallel and distributed computing in this regard. With the former, multiple processors or cores of a single computing unit perform the simultaneous processes. As for distributed computing, it relies on interconnected nodes that only act as a single unit for the same task.
Despite their differences, both parallel and distributed computing systems have a common enemy to concurrency: deadlocks (blocking of two or more processes). When a deadlock occurs, concurrency goes out of the window.
Advantages of Distributed Computing
There are numerous reasons why using distributed computing is a good idea:
- Improved performance. Access to multiple resources means performing at peak capacity, regardless of the workload.
- Resource sharing. Sharing resources between several workstations is your one-way ticket to efficiently completing computation tasks.
- Increased reliability and availability. Unlike single-system computing, distributed computing has no single point of failure. This means welcoming reliability, consistency, and availability and bidding farewell to hardware vulnerabilities and software failures.
- Scalability and flexibility. When it comes to distributed computing, there’s no such thing as too much workload. The system will simply add new nodes and carry on. No centralized system can match this level of scalability and flexibility.
- Cost-effectiveness. Delegating a task to several lower-end computing units is much more cost-effective than purchasing a single high-end unit.
Challenges in Distributed Computing
Although this offers numerous advantages, it’s not always smooth sailing with distributed systems. All involved parties are still trying to address the following challenges:
- Network latency and bandwidth limitations. Not all distributed systems can handle a massive amount of data on time. Even the slightest delay (latency) can affect the system’s overall performance. The same goes for bandwidth limitations (the amount of data that can be transmitted simultaneously).
- Security and privacy concerns. While sharing resources has numerous benefits, it also has a significant flaw: data security. If a system as open as a distributed computing system doesn’t prioritize security and privacy, it will be plagued by data breaches and similar cybersecurity threats.
- Data consistency and synchronization. A distributed computing system derives all its power from its numerous nodes. But coordinating all these nodes (various hardware, software, and network configurations) is no easy task. That’s why issues with data consistency and synchronization (concurrency) come as no surprise.
- System complexity and management. The bigger the distributed computing system, the more challenging it gets to manage it efficiently. It calls for more knowledge, skills, and money.
- Interoperability and standardization. Due to the heterogeneous nature of a distributed computing system, maintaining interoperability and standardization between the nodes is challenging, to say the least.
Applications of Distributed Computing
Nowadays, distributed computing is everywhere. Take a look at some of its most common applications, and you’ll know exactly what we mean:
- Scientific research and simulations. Distributed computing systems model and simulate complex scientific data in fields like healthcare and life sciences. (For example, accelerating patient diagnosis with the help of a large volume of complex images (CT scans, X-rays, and MRIs).
- Big data processing and analytics. Big data sets call for ample storage, memory, and computational power. And that’s precisely what distributed computing brings to the table.
- Content delivery networks. Delivering content on a global scale (social media, websites, e-commerce stores, etc.) is only possible with distributed computing.
- Online gaming and virtual environments. Are you fond of massively multiplayer online games (MMOs) and virtual reality (VR) avatars? Well, you have distributed computing to thank for them.
- Internet of Things (IoT) and smart devices. At its very core, IoT is a distributed system. It relies on a mixture of physical access points and internet services to transform any devices into smart devices that can communicate with each other.
Future Trends in Distributed Computing
Given the flexibility and usability of distributed computing, data scientists and programmers are constantly trying to advance this revolutionary technology. Check out some of the most promising trends in distributed computing:
- Edge computing and fog computing – Overcoming latency challenges
- Serverless computing and Function-as-a-Service (FaaS) – Providing only the necessary amount of service on demand
- Blockchain – Connecting computing resources of cryptocurrency miners worldwide
- Artificial intelligence and machine learning – Improving the speed and accuracy in training models and processing data
- Quantum computing and distributed systems – Scaling up quantum computers
Distributed Computing Is Paving the Way Forward
The ability to scale up computational processes opens up a world of possibilities for data scientists, programmers, and entrepreneurs worldwide. That’s why current challenges and obstacles to distributed computing aren’t particularly worrisome. With a little more research, the trustworthiness of distributed systems won’t be questioned anymore.