Gone are the days when you had to store boxes of documents in your office. Salvation came in the form of cloud computing in the 2000s. Since then, it’s made a world of difference for businesses across all industries, increasing productivity, organization, and decluttering the workspace. More importantly, it allows businesses to reduce various expenses by 30%-50%.

Cloud computing has countless benefits, but that doesn’t mean the technology is flawless. On the contrary, you should be aware of several disadvantages of cloud computing that can cause many problems with your implementation. Weighing up the pros and cons is essential – and we’ll do precisely that in this article.

Read on for the advantages and disadvantages of cloud computing.

Advantages of Cloud Computing

The cloud computing market is worth more than $540 billion. The main reason being that over 90% of all companies use some form of this technology. Here’s why they rely on cloud-based platforms.

Cost Efficiency

One of the greatest benefits of cloud computing is that it’s cost-efficient and allows you to reduce business expenses on three fronts.

Reduced Hardware and Software Expenses

You don’t need physical hardware to store your documents if you have a cloud computing platform. Likewise, the technology eliminates the need to run multiple software platforms because you can keep all your files in one place.

Lower Energy Consumption

In-house storage solutions can be convenient, but they consume a lot of electricity. Conversely, cloud computing systems help companies increase energy efficiency by over 90%.

Minimal Maintenance Costs

Maintaining such platforms is straightforward and affordable as cloud computing doesn’t involve heavy-duty software and hardware.

Scalability and Flexibility

Another reason cloud computing is popular is its scalability and flexibility. Here’s what underpins these advantages of cloud computing.

Easy Resource Allocation and Management

You don’t need to allocate your storage resources to numerous solutions if you have a unified cloud computing system. Managing your storage requirements becomes much easier with all your money going into one channel.

Pay-As-You-Go Pricing Model

Cloud-based platforms are available on a pay-as-you-go model. This reduces the risk of overpaying for your service because you’re only charged for the amount of data used.

Rapid Deployment of Applications and Services

Deploying cloud computing applications and services is simple. There’s no need for intense employee training, which further reduces your costs.

Accessibility and Mobility

Cloud computing is a highly accessible and mobile technology that can elevate your efficiency in a number of ways.

Access to Data and Applications From Anywhere

All it takes to access a cloud-based platform is a stable internet connection. As a result, you can retrieve key files virtually anywhere.

Improved Collaboration and Productivity

The ability to access data and applications from anywhere boosts collaboration and productivity. Your team gets a unified platform where they can share data with others much faster.

Support for Remote Work and Distributed Teams

Setting up a remote workspace is seamless with a cloud-computing solution. Employees no longer have to come to the office to perform repetitive tasks since they can do them from their computers.

Enhanced Security

If you want to address the most common security concerns in your organization, cloud computing is an excellent option.

Centralized Data Storage and Protection

By storing your information in a centralized location, you decrease the risk of data theft. In essence, you funnel all your resources into one platform rather than spread them out across multiple channels.

Regular Security Updates and Patches

Cloud computing providers offer regular updates to protect your information. Systems with the latest security patches are less prone to cyber attacks.

Advanced Encryption and Authentication Methods

You can also benefit from cloud computing tools due to their next-level encryption and authentication solutions. Most platforms feature AES 256-bit encryption, which is the most advanced and practically impregnable method. Furthermore, two-factor authentication lowers the chances of unauthorized access.

Disaster Recovery and Business Continuity

Business continuity and disaster recovery are two of the most pressing business challenges. Cloud computing solutions can help address these problems.

Automated Data Backup and Recovery

Many cloud storage systems are designed to automatically backup and recover your data. Hence, you don’t need to worry about losing your information in the event of a power outage.

Reduced Downtime and Data Loss

Since cloud computing helps prevent data loss, this technology also saves you less downtime. You don’t have to retrieve information manually because the platform does the work for you.

Simplified Disaster Recovery Planning

Although cloud computing tools are reliable, they’re not immune to failure caused by power loss, natural disasters, and other factors. Fortunately, these platforms have robust disaster recovery plans to get your system up and running in no time.

Disadvantages of Cloud Computing

Since the technology is so effective, you might be asking yourself: “Are there any disadvantages of cloud computing?” There are, and you need to understand these downsides to determine the best way to implement the technology. Here are the main drawbacks of cloud computing.

Data Privacy and Security Concerns

Like any other online technology, cloud computing can put users at risk of data privacy and security concerns.

Potential for Data Breaches and Unauthorized Access

While cloud apps have exceptional security practices, cyber criminals can bypass them with state-of-the-art technology and innovative hacking methods. Consequently, they may gain access to your information and steal your credentials.

Compliance With Data Protection Regulations

Your cloud computing tool may comply with many data protection regulations, but this doesn’t mean your information is 100% secure. Some standards only require apps to use robust password practices and fail to consider other attack methods, such as phishing.

Trusting Third-Party Providers With Sensitive Information

Online services require you to share your information to enable all features. Cloud computing is no different in this respect. You need to provide a third-party vendor with your data, which can be risky.

Limited Control and Customization

Cloud computing is a flexible and scalable technology. At the same time, it limits your control and customization options, which is why you might not be 100% happy with your platform.

Dependence on Cloud Service Providers

You decide what files you wish to share with your cloud-based solution. However, that’s pretty much it when it comes to the control you have over the platform. You depend on the vendor for every other aspect, including updates and patches.

Restrictions on Software and Hardware Customization

There aren’t many options to choose from when selecting a cloud storage plan. The price of your plan mostly depends on how much data you wish to share. Other than that, you get little-to-no hardware and software customization features.

Potential for Vendor Lock-In

Once you create an account with one cloud computing provider, you might not be happy with their services. As a result, you want to switch to a different platform. Many people think this is a simple transition, but that’s not always the case. Even though you can cancel your plan, migrating your data from one tool to the next can be difficult.

Network Dependency and Connectivity Issues

You might be relieved once you set up an account on a cloud-based platform: “I no longer need to clutter my office with masses of documents because I can now use an internet tool.” That said, using an online app also means you depend on network quality.

Reliance on Stable Internet Connection

A stable internet connection is essential for cloud computing. Internet problems can reduce or prevent you from accessing your files altogether.

Performance Issues Due to Network Latency

If your cloud network has high latency, sharing files can be challenging. In turn, latency reduces productivity and collaboration.

Vulnerability to Distributed Denial-of-Service (DDoS) Attacks

Cloud platforms are susceptible to so-called DDoS attacks. A cyber criminal can target your tool and keep you from accessing the service.

Downtime and Service Reliability

Not every cloud computing system performs the same in terms of reducing downtime and maximizing reliability.

Risk of Outages and Service Disruptions

While cloud-based solutions have exceptional recovery plans and backup methods, you’ll still face some downtime in case of outages. Even the shortest service disruption can cause major issues when working on certain projects.

Shared Resources and Potential for Performance Degradation

Cloud systems are convenient because they allow you to store your data in one place. Nonetheless, one of the key disadvantages of cloud computing is managing those shared resources. Accessing information can become difficult if you don’t stay on top of it.

Likewise, performance can drop at any point of your plan. App incompatibility and other issues can compromise data architecture and further compromise management.

Dependence on Provider’s Service Level Agreements (SLAs)

You’ll probably need to enter into an SLA when partnering with a cloud computing provider. These contracts can be rigid, meaning they may fail to recognize and adapt to evolving business needs.

Make an Informed Decision

Cloud computing has tremendous benefits, like improved data storage, collaboration, and cost reduction. The main drawbacks include hardware and software restrictions, connectivity issues, and potential downtime.

Therefore, you should understand the advantages and disadvantages of cloud computing before implementing a platform. Also, consider your business needs when partnering with a cloud provider to help prevent compatibility issues.

As computing technology evolved and the concept of linking multiple computers together into a “network” that could share data came into being, it was clear that a model was needed to define and enable those connections. Enter the OSI model in computer network idea.

This model allows various devices and software to “communicate” with one another by creating a set of universal rules and functions. Let’s dig into what the model entails.

History of the OSI Model

In the late 1970s, the continued development of computerized technology saw many companies start to introduce their own systems. These systems stood alone from others. For example, a computer at Retailer A has no way to communicate with a computer at Retailer B, with neither computer being able to communicate with the various vendors and other organizations within the retail supply chain.

Clearly, some way of connecting these standalone systems was needed, leading to researchers from France, the U.S., and the U.K. splitting into two groups – The International Organization for Standardization and the International Telegraph and Telephone Consultive Committee.

In 1983, these two groups merged their work to create “The Basic Reference Model for Open Systems Interconnection (OSI).” This model established industry standards for communication between networked devices, though the path to OSI’s implementation wasn’t as clear as it could have been. The 1980s and 1990s saw the introduction of another model – The TCP IP model – which competed against the OSI model for supremacy. TCP/IP gained so much traction that it became the cornerstone model for the then-budding internet, leading to the OSI model in computer network applications falling out of favor in many sectors. Despite this, the OSI model is still a valuable reference point for students who want to learn more about networking and still have some practical uses in industry.

The OSI Reference Model

The OSI model works by splitting the concept of computers communicating with one another into seven computer network layers (defined below), each offering standardized rules for its specific function. During the rise of the OSI model, these layers worked in concert, allowing systems to communicate as long as they followed the rules.

Though the OSI model in computer network applications has fallen out of favor on a practical level, it still offers several benefits:

• The OSI model is perfect for teaching network architecture because it defines how computers communicate.
• OSI is a layered model, with separation between each layer, so one layer doesn’t affect the operation of any other.
• The OSI model offers flexibility because of the distinctions it makes between layers, with users being able to replace protocols in any layer without worrying about how they’ll impact the other layers.

The 7 Layers of the OSI Model

The OSI reference model in computer network teaching is a lot like an onion. It has several layers, each standing alone but each needing to be peeled back to get a result. But where peeling back the layers of an onion gets you a tasty ingredient or treat, peeling them back in the OSI model delivers a better understanding of networking and the protocols that lie behind it.

Each of these seven layers serves a different function.

Layer 1: Physical Layer

Sitting at the lowest level of the OSI model, the physical layer is all about the hows and wherefores of transmitting electrical signals from one device to another. Think of it as the protocols needed for the pins, cables, voltages, and every other component of a physical device if said device wants to communicate with another that uses the OSI model.

Layer 2: Data Link Layer

With the physical layer in place, the challenge shifts to transmitting data between devices. The data layer defines how node-to-node transfer occurs, allowing for the packaging of data into “frames” and the correction of errors that may happen in the physical layer.

The data layer has two “sub-layers” of its own:

• MAC – Media Access Controls that offer multiplexing and flow control to govern a device’s transmissions over an OSI network.
• LLC – Logical Link Controls that offer error control over the physical media (i.e., the devices) used to transmit data across a connection.

Layer 3: Network Layer

The network layer is like an intermediary between devices, as it accepts “frames” from the data layer and sends them on their way to their intended destination. Think of this layer as the postal service of the OSI model in computer network applications.

Layer 4: Transport Layer

If the network layer is a delivery person, the transport layer is the van that the delivery person uses to carry their parcels (i.e., data packets) between addresses. This layer regulates the sequencing, sizing, and transferring of data between hosts and systems. TCP (Transmission Control Protocol) is a good example of a transport layer in practical applications.

Layer 5: Session Layer

When one device wants to communicate with another, it sets up a “session” in which the communication takes place, similar to how your boss may schedule a meeting with you when they want to talk. The session layer regulates how the connections between machines are set up and managed, in addition to providing authorization controls to ensure no unwanted devices can interrupt or “listen in” on the session.

Layer 6: Presentation Layer

Presentation matters when sending data from one system to another. The presentation layer “pretties up” data by formatting and translating it into a syntax that the recipient’s application accepts. Encryption and decryption is a perfect example, as a data packet can be encrypted to be unreadable to anybody who intercepts it, only to be decrypted via the presentation layer so the intended recipient can see what the data packet contains.

Layer 7: Application Layer

The application layer is a front end through which the end user can interact with everything that’s going on behind the scenes in the network. It’s usually a piece of software that puts a user-friendly face on a network. For instance, the Google Chrome web browser is an application layer for the entire network of connections that make up the internet.

Interactions Between OSI Layers

Though each of the OSI layers in computer networks is independent (lending to the flexibility mentioned earlier), they must also interact with one another to make the network functional.

We see this most obviously in the data encapsulation and de-encapsulation that occurs in the model. Encapsulation is the process of adding information to a data packet as it travels, with de-encapsulation being the method used to remove that data added data so the end user can read what was originally sent. The previously mentioned encryption and decryption of data is a good example.

That process of encapsulation and de-encapsulation defines how the OSI model works. Each layer adds its own little “flavor” to the transmitted data packet, with each subsequent layer either adding something new or de-encapsulating something previously added so it can read the data. Each of these additions and subtractions is governed by the protocols set within each layer. A perfect network can only exist if these protocols properly govern data transmission, allowing for communication between each layer.

Real-World Applications of the OSI Model

There’s a reason why the OSI model in computer network study is often called a “reference” model – though important, it was quickly replaced with other models. As a result, you’ll rarely see the OSI model used as a way to connect devices, with TCP/IP being far more popular. Still, there are several practical applications for the OSI model.

Network Troubleshooting and Diagnostics

Given that some modern computer networks are unfathomably complex, picking out a single error that messes up the whole communication process can feel like navigating a minefield. Every wrong step causes something else to blow up, leading to more problems than you solve. The OSI model’s layered approach offers a way to break down the different aspects of a network to make it easier to identify problems.

Network Design and Implementation

Though the OSI model has few practical purposes, as a theoretical model it’s often seen as the basis for all networking concepts that came after. That makes it an ideal teaching tool for showcasing how networks are designed and implemented. Some even refer to the model when creating networks using other models, with the layered approach helping understand complex networks.

Enhancing Network Security

The concept of encapsulation and de-encapsulation comes to the fore again here (remember – encryption), as this concept shows us that it’s dangerous to allow a data packet to move through a network with no interactions. The OSI model shows how altering that packet as it goes on its journey makes it easier to protect data from unwanted eyes.

Limitations and Criticisms of the OSI Model

Despite its many uses as a teaching tool, the OSI model in computer network has limitations that are the reasons why it sees few practical applications:

• Complexity – As valuable as the layered approach may be to teaching networks, it’s often too complex to execute in practice.
• Overlap – The very flexibility that makes OSI great for people who want more control over their networks can come back to bite the model. The failure to implement proper controls and protocols can lead to overlap, as can the layered approach itself. Each of the computer network layers needs the others to work.
• The Existence of Alternatives – The OSI model walked so other models could run, establishing many fundamental networking concepts that other models executed better in practical terms. Again, the massive network known as the internet is a great example, as it uses the TCP/IP model to reduce complexity and more effectively transmit data.

Use the OSI Reference Model in Computer Network Applications

Though it has little practical application in today’s world, the OSI model in computer network terms is a theoretical model that played a crucial role in establishing many of the “rules” of networking still used today. Its importance is still recognized by the fact that many computing courses use the OSI model to teach the fundamentals of networks.

Think of learning about the OSI model as being similar to laying the foundations for a house. You’ll get to grips with the basic concepts of how networks work, allowing you to build up your knowledge by incorporating both current networking technology and future advancements to become a networking specialist.

Computer architecture forms the backbone of computer science. So, it comes as no surprise it’s one of the most researched fields of computing.

But what is computer architecture, and why does it matter?

Basically, computer architecture dictates every aspect of a computer’s functioning, from how it stores data to what it displays on the interface. Not to mention how the hardware and software components connect and interact.

With this in mind, it isn’t difficult to realize the importance of this structure. In fact, computer scientists did this even before they knew what to call it. The first documented computer architecture can be traced back to 1936, 23 years before the term “architecture” was first used when describing a computer. Lyle R. Johnson, an IBM senior staff member, had this honor, realizing that the word organization just doesn’t cut it.

Now that you know why you should care about it, let’s define computer architecture in more detail and outline everything you need to know about it.

Basic Components of Computer Architecture

Computer architecture is an elaborate system where each component has its place and function. You’re probably familiar with some of the basic computer architecture components, such as the CPU and memory. But do you know how those components work together? If not, we’ve got you covered.

Central Processing Unit (CPU)

The central processing unit (CPU) is at the core of any computer architecture. This hardware component only needs instructions written as binary bits to control all its surrounding components.

Think of the CPU as the conductor in an orchestra. Without the conductor, the choir is still there, but they’re waiting for instructions.

Without a functioning CPU, the other components are still there, but there’s no computing.

That’s why the CPU’s components are so important.

Arithmetic Logic Unit (ALU)

Since the binary bits used as instructions by the CPU are numbers, the unit needs an arithmetic component to manipulate them.

That’s where the arithmetic logic unit, or ALU, comes into play.

The ALU is the one that receives the binary bits. Then, it performs an operation on one or more of them. The most common operations include addition, subtraction, AND, OR, and NOT.

Control Unit (CU)

As the name suggests, the control unit (CU) controls all the components of basic computer architecture. It transfers data to and from the ALU, thus dictating how each component behaves.

Registers

Registers are the storage units used by the CPU to hold the current data the ALU is manipulating. Each CPU has a limited number of these registers. For this reason, they can only store a limited amount of data temporarily.

Memory

Storing data is the main purpose of the memory of a computer system. The data in question can be instructions issued by the CPU or larger amounts of permanent data. Either way, a computer’s memory is never empty.

Traditionally, this component can be broken into primary and secondary storage.

Primary Memory

Primary memory occupies a central position in a computer system. It’s the only memory unit that can communicate with the CPU directly. It stores only programs and data currently in use.

There are two types of primary memory:

• RAM (Random Access Memory). In computer architecture, this is equivalent to short-term memory. RAM helps start the computer and only stores data as long as the machine is on and data is being used.
• ROM (Read Only Memory). ROM stores the data used to operate the system. Due to the importance of this data, the ROM stores information even when you turn off the computer.

Secondary Memory

With secondary memory, or auxiliary memory, there’s room for larger amounts of data (which is also permanent). However, this also means that this memory is significantly slower than its primary counterpart.

When it comes to secondary memory, there’s no shortage of choices. There are magnetic discs (hard disk drives (HDDs) and solid-state drives (SSDs)) that provide fast access to stored data. And let’s not forget about optical discs (CD-ROMs and DVDs) that offer portable data storage.

Input/Output (I/O) Devices

The input/output devices allow humans to communicate with a computer. They do so by delivering or receiving data as necessary.

You’re more than likely familiar with the most widely used input devices – the keyboard and the mouse. When it comes to output devices, it’s pretty much the same. The monitor and printer are at the forefront.

Buses

When the CPU wants to communicate with other internal components, it relies on buses.

Data buses are physical signal lines that carry data. Most computer systems use three of these lines:

• Data bus – Transmitting data from the CPU to memory and I/O devices and vice versa
• Address bus – Carrying the address that points to the location the CPU wants to access
• Control bus – Transferring control from one component to the other

Types of Computer Architecture

There’s more than one type of computer architecture. These types mostly share the same base components. However, the setup of these components is what makes them differ.

Von Neumann Architecture

The Von Neumann architecture was proposed by one of the originators of computer architecture as a concept, John Von Neumann. Most modern computers follow this computer architecture.

The Von Neumann architecture has several distinguishing characteristics:

• All instructions are carried out sequentially.
• It doesn’t differentiate between data and instruction. They’re stored in the same memory unit.
• The CPU performs one operation at a time.

Since data and instructions are located in the same place, fetching them is simple and efficient. These two adjectives can describe working with the Von Neumann architecture in general, making it such a popular choice.

Still, there are some disadvantages to keep in mind. For starters, the CPU is often idle since it can only access one bus at a time. If an error causes a mix-up between data and instructions, you can lose important data. Also, defective programs sometimes fail to release memory, causing your computer to crash.

Harvard Architecture

Harvard architecture was named after the famed university. Or, to be more precise, after an IBM computer called “Harvard Mark I” located at the university.

The main difference between this computer architecture and the Von Neumann model is that the Harvard architecture separates the data from the instructions. Accordingly, it allocates separate data, addresses, and control buses for the separate memories.

The biggest advantage of this setup is that the buses can fetch data concurrently, minimizing idle time. The separate buses also reduce the chance of data corruption.

However, this setup also requires a more complex architecture that can be challenging to develop and implement.

Modified Harvard Architecture

Today, only specialty computers use the pure form of Harvard architecture. As for other machines, a modified Harvard architecture does the trick. These modifications aim to soften the rigid separation between data and instructions.

RISC and CISC Architectures

When it comes to processor architecture, there are two primary approaches.

The CISC (Complex Instruction Set Computer) processors have a single processing unit and are pretty straightforward. They tackle one task at a time. As a result, they use less memory. However, they also need more time to complete an instruction.

Over time, the speed of these processors became a problem. This led to a processor redesign, resulting in the RISC architecture.

The new and improved RISC (Reduced Instruction Set Computer) processors feature larger registers and keep frequently used variables within the processor. Thanks to these handy functionalities, they can operate much more quickly.

Instruction Set Architecture (ISA)

Instruction set architecture (ISA) defines the instructions that the processor can read and act upon. This means ISA decides which software can be installed on a particular processor and how efficiently it can perform tasks.

There are three types of instruction set architecture. These types differ based on the placement of instructions, and their names are pretty self-explanatory. For stack-based ISA, the instructions are placed in the stack, a memory unit within the address register. The same principle applies for accumulator-based ISA (a type of register in the CPU) and register-based ISA (multiple registers within the system).

The register-based ISA is most commonly used in modern machines. You’ve probably heard of some of the most popular examples. For CISC architecture, there are x86 and MC68000. As for RISC, SPARC, MIPS, and ARM stand out.

Pipelining and Parallelism in Computer Architecture

In computer architecture, pipelining and parallelism are methods used to speed up processing.

Pipelining refers to overlapping multiple instructions and processing them simultaneously. This couldn’t be possible without a pipeline-like structure. Imagine a factory assembly line, and you’ll understand how pipelining works instantly.

This method significantly increases the number of processed instructions and comes in two types:

• Instruction pipelines – Used for fixed-point multiplication, floating-point operations, and similar calculations
• Arithmetic pipelines – Used for reading consecutive instructions from memory

Parallelism entails using multiple processors or cores to process data simultaneously. Thanks to this collaborative approach, large amounts of data can be processed quickly.

Computer architecture employs two types of parallelism:

• Data parallelism – Executing the same task with multiple cores and different sets of data
• Task parallelism – Performing different tasks with multiple cores and the same or different data

Multicore processors are crucial for increasing the efficiency of parallelism as a method.

Memory Hierarchy and Cache

In computer system architecture, memory hierarchy is essential for minimizing the time it takes to access the memory units. It refers to separating memory units based on their response times.

The most common memory hierarchy goes as follows:

• Level 1: Processor registers
• Level 2: Cache memory
• Level 3: Primary memory
• Level 4: Secondary memory

The cache memory is a small and fast memory located close to a processor core. The CPU uses it to reduce the time and energy needed to access data from the primary memory.

Cache memory can be further broken into levels.

• L1 cache (the primary cache) – The fastest cache unit in the system
• L2 cache (the secondary cache) – The slower but more spacious option than Level 1
• L3 cache (a specialized cache) – The largest and the slowest cache in the system used to improve the performance of the first two levels

When it comes to determining where the data will be stored in the cache memory, three mapping techniques are employed:

• Direct mapping – Each memory block is mapped to one pre-determined cache location
• Associative mapping – Each memory block is mapped to a single location, but it can be any location
• Set associative mapping – Each memory block is mapped to a subset of locations

The performance of cache memory directly impacts the overall performance of a computing system. The following cache replacement policies are used to better process big data applications:

• FIFO (first in, first out) ­– The memory block first to enter the primary memory gets replaced first
• LRU (least recently used) – The least recently used page is the first to be discarded
• LFU (least frequently used) – The least frequently used element gets eliminated first

Input/Output (I/O) Systems

The input/output or I/O systems are designed to receive and send data to a computer. Without these processing systems, the computer wouldn’t be able to communicate with people and other systems and devices.

There are several types of I/O systems:

• Programmed I/O – The CPU directly issues a command to the I/O module and waits for it to be executed
• Interrupt-Driven I/O – The CPU moves on to other tasks after issuing a command to the I/O system
• Direct Memory Access (DMA) – The data is transferred between the memory and I/O devices without passing through the CPU

There are three standard I/O interfaces used for physically connecting hardware devices to a computer:

• Peripheral Component Interconnect (PCI)
• Small Computer System Interface (SATA)
• Universal Serial Bus (USB)

Power Consumption and Performance in Computer Architecture

Power consumption has become one of the most important considerations when designing modern computer architecture. Failing to consider this aspect leads to power dissipation. This, in turn, results in higher operating costs and a shorter lifespan for the machine.

For this reason, the following techniques for reducing power consumption are of utmost importance:

• Dynamic Voltage and Frequency Scaling (DVFS) – Scaling down the voltage based on the required performance
• Clock gating – Shutting off the clock signal when the circuit isn’t in use
• Power gating – Shutting off the power to circuit blocks when they’re not in use

Besides power consumption, performance is another crucial consideration in computer architecture. The performance is measured as follows:

• Instructions per second (IPS) – Measuring efficiency at any clock frequency
• Floating-point operations per second (FLOPS) – Measuring the numerical computing performance
• Benchmarks – Measuring how long the computer takes to complete a series of test programs

Emerging Trends in Computer Architecture

Computer architecture is continuously evolving to meet modern computing needs. Keep your eye out on these fascinating trends:

• Quantum computing (relying on the laws of quantum mechanics to tackle complex computing problems)
• Neuromorphic computing (modeling the computer architecture components on the human brain)
• Optical computing (using photons instead of electrons in digital computation for higher performance)
• 3D chip stacking (using 3D instead of 2D chips as they’re faster, take up less space, and require less power)

A One-Way Ticket to Computing Excellence

As you can tell, computer architecture directly affects your computer’s speed and performance. This launches it to the top of priorities when building this machine.

High-performance computers might’ve been nice-to-haves at some point. But in today’s digital age, they’ve undoubtedly become a need rather than a want.

In trying to keep up with this ever-changing landscape, computer architecture is continuously evolving. The end goal is to develop an ideal system in terms of speed, memory, and interconnection of components.

And judging by the current dominant trends in this field, that ideal system is right around the corner!

Thanks to many technological marvels of our era, we’ve moved from writing important documents using pen and paper to storing them digitally.

Database systems emerged as the amount and complexity of information we need to keep have increased significantly in the last decades. They represent virtual warehouses for storing documents. Database management systems (DBMS) and relational database management systems (RDBMS) were born out of a burning need to easily control, organize, and edit databases.

Both DBMS and RDBMS represent programs for managing databases. But besides the one letter in the acronym, the two terms differ in several important aspects.

Here, we’ll outline the difference between DBMS and RDBMS, help you learn the ins and outs of both, and choose the most appropriate one.

Definition of DBMS (Database Management Systems)

While working for General Electric during the 1960s, Charles W. Bachman recognized the importance of proper document management and found that the solutions available at the time weren’t good enough. He did his research and came up with a database management system, a program that made storing, editing, and retrieving files a breeze. Unknowingly, Bachman revolutionized the industry and offered the world a convenient database management solution with amazing properties.

Key Features

Over the years, DBMSs have become powerful beasts that allow you to enhance performance and efficiency, save time, and handle huge amounts of data with ease.

One of the key features of DBMSs is that they store information as files in one of two forms: hierarchical or navigational. When managing data, users can use one of several manipulation functions the systems offer:

• Inserting data
• Deleting data
• Updating data

DBMSs are simple structures ideal for smaller companies that don’t deal with huge amounts of data. Only a single user can handle information, which can be a deal-breaker for larger entities.

Although fairly simple, DBMSs bring a lot to the table. They allow you to access, edit, and share data in the blink of an eye. Moreover, DBMSs let you unify your team and have accurate and reliable information on the record, ensuring nobody is left out. They also help you stay compliant with different security and privacy regulations and lower the risk of violations. Finally, having an efficient database management system leads to wiser decision-making that can ultimately save you a lot of time and money.

Examples of Popular DBMS Software

When DBMSs were just becoming a thing, you had software like Clipper and FoxPro. Today, the most popular (and simplest) examples of DBMS software are XML, Windows Registry, and file systems.

Definition of RDBMS (Relational Database Management Systems)

Not long after DBMS came into being, people recognized the need to keep data in the form of tables. They figured storing info in rows (tuples) and columns (attributes) allows a clearer view and easier navigation and information retrieval. This idea led to the birth of relational database management systems (RDBMS) in the 1970s.

Key Features

As mentioned, the only way RDBMSs store information is in the form of tables. Many love this feature because it makes organizing and classifying data according to different criteria a piece of cake. Many companies that use RDBMSs utilize multiple tables to store their data, and sometimes, the information in them can overlap. Fortunately, RDBMSs allow relating data from various tables to one another (hence the name). Thanks to this, you’ll have no trouble adding the necessary info in the right tables and moving it around as necessary.

Since you can relate different pieces of information from your tables to each other, you can achieve normalization. However, normalization isn’t the process of making your table normal. It’s a way of organizing information to remove redundancy and enhance data integrity.

In this technological day and age, we see data growing exponentially. If you’re working with RDBMSs, there’s no need to be concerned. The systems can handle vast amounts of information and offer exceptional speed and total control. Best of all, multiple users can access RDBMSs at a time and enhance your team’s efficiency, productivity, and collaboration.

Simply put, an RDBMS is a more advanced, powerful, and versatile version of DBMS. It offers speed, plenty of convenient features, and ease of use.

Examples of Popular RDBMS Software

As more and more companies recognize the advantages of using RDBMS, the availability of software grows by the day. Those who have tried several options agree that Oracle and MySQL are among the best choices.

Key Differences Between DBMS and RDBMS

Now that you’ve learned more about DBMS and RDBMS, you probably have an idea of the most significant differences between them. Here, we’ll summarize the key DBMS vs. RDBMS differences.

Data Storage and Organization

The first DBMS and RDBMS difference we’ll analyze is the way in which the systems store and organize information. With DBMS, data is stored and organized as files. This system uses either a hierarchical or navigational form to arrange the information. With DBMS, you can access only one element at a time, which can lead to slower processing.

On the other hand, RDBMS uses tables to store and display information. The data featured in several tables can be related to each other for ease of use and better organization. If you want to access multiple elements at the same time, you can; there are no constraints regarding this, as opposed to DBMS.

Data Integrity and Consistency

When discussing data integrity and consistency, it’s necessary to explain the concept of constraints in DBMS and RDBMS. Constraints are sets of “criteria” applied to data and/or operations within a system. When constraints are in place, only specific types of information can be displayed, and only specific operations can be completed. Sounds restricting, doesn’t it? The entire idea behind constraints is to enhance the integrity, consistency, and correctness of data displayed within a database.

DBMS lacks constraints. Hence, there’s no guarantee the data within this system is consistent or correct. Since there are no constraints, the risk of errors is higher.

RDBMS have constraints, resulting in the reliability and integrity of the data. Plus, normalization (removing redundancies) is another option that contributes to data integrity in RDBMS. Unfortunately, normalization can’t be achieved in DBMS.

Query Language and Data Manipulation

DBMS uses multiple query languages to manipulate data. However, none of these languages offer the speed and convenience present in RDBMS.

RDBMS manipulates data with structured query language (SQL). This language lets you retrieve, create, insert, or drop data within your relational database without difficulty.

Scalability and Performance

If you have a small company and/or don’t need to deal with vast amounts of data, a DBMS can be the way to go. But keep in mind that a DBMS can only be accessed by one person at a time. Plus, there’s no option to access more than one element at once.

With RDBMSs, scalability and performance are moved to a new level. An RDBMS can handle large amounts of information in a jiff. It also supports multiple users and allows you to access several elements simultaneously, thus enhancing your efficiency. This makes RDBMSs excellent for larger companies that work with large quantities of data.

Security and Access Control

Last but not least, an important difference between DBMS and RDBMS lies in security and access control. DBMSs have basic security features. Therefore, there’s a higher chance of breaches and data theft.

RDBMSs have various security measures in place that keep your data safe at all times.

Choosing the Right Database Management System

The first criterion that will help you make the right call is your project’s size and complexity. Small projects with relatively simple data are ideal for DBMSs. But if you’re tackling a lot of complex data, RDBMSs are the logical option.

Next, consider your budget and resources. Since they’re simpler, DBMSs are more affordable, in both aspects. RDBMSs are more complex, so naturally, the price of software is higher.

Finally, the factor that affects what option is the best for you is the desired functionality. What do you want from the program? Is it robust features or a simple environment with a few basic options? Your answer will guide you in the right direction.

Pros and Cons of DBMS and RDBMS

DBMS

Pros:

• Doesn’t involve complex query processing
• Cost-effective solution
• Ideal for processing small data
• Easy data handling via basic SQL queries

Cons:

• Doesn’t allow accessing multiple elements at once
• No way to relate data
• Doesn’t inherently support normalization
• Higher risk of security breaches
• Single-user system

RDBMS

Pros:

• Advanced, robust, and well-organized
• Ideal for large quantities of information
• Data from multiple tables can be related
• Multi-user system
• Supports normalization

Cons:

• More expensive
• Complex for some people

Examples of Use Cases

DBMS

DBMS is used in many sectors where more basic storing and management of data is required, be it sales and marketing, education, banking, or online shopping. For instance, universities use DBMS to store student-related data, such as registration details, fees paid, attendance, exam results, etc. Libraries use it to manage the records of thousands of books.

RDBMS

RDBMS is used in many industries today, especially those continuously requiring processing and storing large volumes of data. For instance, Airline companies utilize RDBMS for passenger and flight-related information and schedules. Human Resource departments use RDBMS to store and manage information related to employees and their payroll statistics. Manufacturers around the globe use RDBMS for operational data, inventory management and supply chain information.

Choose the Best Solution

An RDBM is a more advanced and powerful younger sibling of a DBMS. While the former offers more features, convenience, and the freedom to manipulate data as you please, it isn’t always the right solution. When deciding which road to take, prioritize your needs.

In a database, you have entities (which have attributes), and relationships between those entities. Managing them is key to preventing chaos from engulfing your database, which is where the concept of keys comes in. These unique identifiers enable you to pick specific rows in an entity set, as well as define their relationships to rows in other entity sets, allowing your database to handle complex computations.

Let’s explore keys in DBMS (database management systems) in more detail, before digging into everything you need to know about the most important keys – primary keys.

Understanding Keys in DBMS

Keys in DBMS are attributes that you use to identify specific rows inside a table, in addition to finding the relation between two tables. For example, let’s say you have a table for students, with that table recording each student’s “ID Number,” “Name,” “Address,” and “Teacher” as attributes. If you want to identify a specific student in the table, you’ll need to use one of these attributes as a key that allows you to pull the student’s record from your database. In this case “ID Number” is likely the best choice because it’s a unique attribute that only applies to a single student.

Types of Keys in DBMS

Beyond the basics of serving as unique identifiers for rows in a database, keys in DBMS can take several forms:

• Primary Keys – An attribute that is present in the table for all of the records it contains, with each instance of that attribute being unique to the record. The previously-mentioned “ID Number” for students is a great example, as no student can have the same number as another student.
• Foreign Key – Foreign keys allow you to define and establish relationships between a pair of tables. If Table A needs to refer to the primary key in Table B, you’ll use a foreign key in Table A so you have values in that table to match those in Table B.
• Unique Key – These are very similar to primary keys in that both contain unique identifiers for the records in a table. The only difference is that a unique key can contain a null value, whereas a primary key can’t.
• Candidate Key – Though you may have picked a unique attribute to serve as your primary key, there may be other candidates within a table. Coming back to the student example, you may record the phone numbers and email addresses of your students, which can be as unique as the student ID assigned to the individual. These candidate keys are also unique identifiers, allowing them to be used in tandem with a primary key to identify a specific row in a table.
• Composite Key – If you have attributes that wouldn’t be unique when taken alone, but can be combined to form a unique identifier for a record, you have a composite key.
• Super Key – This term refers to the collection of attributes that uniquely identify a record, meaning it’s a combination of candidate keys. Just like an employer sifting through job candidates to find the perfect person, you’ll sift through your super key set to choose the ideal primary key amongst your candidate keys.

So, why are keys in DBMS so important?

Keys ensure you maintain data integrity across all of the tables that make up your database. Without them, the relationships between each table become messy hodgepodges, creating the potential for duplicate records and errors that deliver inaccurate reports from the database. Having unique identifiers (in the form of keys) allows you to be certain that any record you pull, and the relationships that apply to that record, are accurate and unrepeated.

Primary Key Essentials

As mentioned, any unique attribute in a table can serve as a primary key, though this doesn’t mean that every unique attribute is a great choice. The following characteristics help you to define the perfect primary key.

Uniqueness

If your primary key is repeatable across records, it can’t serve as a unique identifier for a single record. For example, our student table may have multiple people named “John,” so you can’t use the “Name” attribute to find a specific student. You need something unique to that student, such as the previously mentioned ID number.

Non-Null Values

Primary keys must always contain a value, else you risk losing records in a table because you have no way of calling upon them. This need for non-null values can be used to eliminate some candidates from primary key content. For instance, it’s feasible (though unlikely) that a student won’t have an email address, creating the potential for null values that mean the email address attribute can’t be a primary key.

Immutability

A primary key that can change over time is a key that can cause confusion. Immutability is the term used for any attribute that’s unchanging to the point where it’s an evergreen attribute that you can use to identify a specific record forever.

Minimal

Ideally, one table should have one attribute that serves as its primary key, which is where the term “minimal” comes in. It’s possible for a table to have a composite or super key set, though both create the possibility of confusion and data integrity issues.

The Importance of a Primary Key in DBMS

We can distill the reason why having a primary key in DBMS for each of your tables is important into the following reasons:

• You can use a primary key to identify each unique record in a table, meaning no multi-result returns to your database searches.
• Having a primary key means a record can’t be repeated in the table.
• Primary keys make data retrieval more efficient because you can use a single attribute for searches rather than multiple.

Functions of Primary Keys

Primary keys in DBMS serve several functions, each of which is critical to your DBMS.

Data Identification

Imagine walking into a crowded room and shouting out a name. The odds are that several people (all of whom have the same name) will turn their heads to look at you. That’s basically what you’re doing if you try to pull records from a table without using a primary key.

A primary key in DBMS serves as a unique identifier that you can use to pull specific records. Coming back to the student example mentioned earlier, a “Student ID” is only applicable to a single student, making it a unique identifier you can use to find that student in your database.

Ensure Data Integrity

Primary keys protect data integrity in two ways.

First, they prevent duplicate records from building up inside a single table, ensuring you don’t get multiple instances of the same record. Second, they ensure referential integrity, which is the term used to describe what happens when one table in your database needs to refer to the records stored in another table.

For example, let’s say you have tables for “Students” and “Teachers” in your database. The primary keys assigned to your students and teachers allow you to pull individual records as needed from each table. But every “Teacher” has multiple “Students” in their class. So, your primary key from the “Students” table is used as a foreign key in the “Teachers” table, allowing you to denote the one-to-many relationship between a teacher and their class of students. That foreign key also ensures referential integrity because it contains the unique identifiers for students, which you can look up in your “Students” table.

Data Retrieval

If you need to pull a specific record from a table, you can’t rely on attributes that can repeat across several records in that table. Again, the “Name” example highlights the problem here, as several people could have the same name. You need a unique identifier for each record so you can retrieve a single record from a huge set without having to pore through hundreds (or even thousands) of records.

Best Practices for Primary Key Selection

Now that you understand how primary keys in DBMS work, here are some best practices for selecting the right primary key for your table:

• Choose Appropriate Attributes as Candidates – If the attribute isn’t unique to each record, or it can contain a null value (as is the case with email addresses and phone numbers), it’s not a good candidate for a primary key.
• Avoid Using Sensitive Information – Using personal or sensitive information as a primary key creates a security risk because anybody who cracks your database could use that information for other purposes. Make your primary keys unique, and only applicable, to your database, which allows you to encrypt any sensitive information stored in your tables.
• Consider Surrogate Keys – Some tables don’t have natural attributes that you can use as primary keys. In these cases, you can create a primary key out of thin air and assign it to each record. The “Student ID” referenced earlier is a great example, as students entering a school don’t come with their own ID numbers. Those numbers are given to the student (or simply used in the database that collects their data), making them surrogate keys.
• Ensure Primary Key Stability – Any attribute that can change isn’t suitable for use as a primary key because it causes stability issues. Names, email addresses, phone numbers, and even bank account details are all things that can change, making them unsuitable. Evergreen and unchanging is the way to go with primary keys.

Choose the Right Keys for Your Database

You need to understand the importance of a primary key in DBMS (or multiple primary keys when you have several tables) so you can define the relationships between tables and identify unique records inside your tables. Without primary keys, you’ll find it much harder to run reports because you won’t feel confident in the accuracy of the data returned. Each search may pull up duplicate or incorrect records because of a lack of unique identifiers.

Thankfully, many of the tables you create will have attributes that lend themselves well to primary key status. And even when that isn’t the case, you can use surrogate keys in DBMS to assign primary keys to your tables. Experiment with your databases, testing different potential primary keys to see what works best for you.

When you first get into modern computing, one of the terms that comes up most frequently is relational databases. These are clusters that are organized in such a way that they effortlessly find links between connected data points.

Relational databases are convenient, but what happens when you deal with vast amounts of information? You need something to act as your North Star, guiding you through the network and allowing you to stay on top of the data.

That something is an RDBMS. According to Google, RDBMS stands for a relational database management system – software that sets up and manages relational databases. In its full form, it’s been the light at the end of the tunnel for thousands of companies due to its accuracy, security, and ease of use.

The definition and importance of RDBMSs are the tip of the iceberg when it comes to these systems. This introduction to RDBMS will delve a bit deeper by taking a closer look at the concept of RDBMS, the history of this technology, use cases, and the most common examples.

History of RDBMS

The concept of RDBMS might be shrouded in mystery for some. Thus, several questions may come up when discussing the notion, including one as basic as “What is RDBMS?”

Knowing the RDBMS definition is a great starting point on your journey to understanding this concept. But let’s take a few steps back and delve into the history of this system.

Origins of the Relational Model

What if we told you that the RDBMS concepts are older than the internet? It may sound surprising, but it’s true.

The concept of RDBMS was developed by Edgar F. Codd 43 years ago. He aimed to propose a more efficient way to store information, a method that would consume drastically less memory than anything at the time. His model was groundbreaking, to say the least.

E.F. Codd’s Paper on Relational Model

Codd laid down his proposal in a 1970s paper called “A Relational Model of Data for Large Shared Data Banks.” He advocated a database solution comprised of intertwined tables. These tables enabled the user to keep their information compact, lowering the amount of disk space necessary for storage (which was scarce at the time).

The rest is history. The public welcomed Codd’s model with open arms since it optimized storage requirements and allowed people to answer practically any question using his principle.

Development of SQL

Codd’s research paved the way for relational database management systems, the most famous of which is SQL. This programming language was also developed in the ‘70s and was originally named SEQUEL (Structured English Query Language). It was quickly implemented across the computing industry and grew more powerful as the years went by.

Evolution of RDBMS Software

The evolution of RDBMS software has been fascinating.

Early RDBMS Software

The original RDBMS software was powerful, but it wasn’t a cure-all. It was a match made in heaven for users dealing with structured data, allowing them to organize it with minimal effort. However, pictures, music, and other forms of unstructured information were largely incompatible with this model.

Modern RDBMS Software

Today’s RDBMS solutions have come a long way from their humble beginnings. A modern relational DBMS can process different forms of information with ease. Programs like MySQL are versatile, adaptable, and easy to set up, helping database professionals spearhead the development of practically any application.

Key Concepts in RDBMS

Here’s another request you may have for an expert in RDBMS – explain the most significant relational database concepts. If that’s your question, your request has been granted. Coming up is an overview of RDBMS concepts that explain RDBMS in simple terms.

Tables and Relations

Tables and relations are the bread and butter of all relational database management systems. They sound straightforward, but they’re much different from, say, elements you come across in Microsoft Excel.

Definition of Tables

Tables are where data is stored in an RDBMS. They’re comprised of rows and columns for easier organization.

Definition of Relations

Relations are the links between tables. There can be several types of relations, such as one-to-one connections. This form means a data point from one table only matches one data point from another table.

Primary and Foreign Keys

No discussion about RDBMS solutions is complete without primary and foreign keys.

Definition of Primary Keys

A primary key is the unique element of each table that defines the table’s rows. The number of primary keys in a table is limited to one.

Definition of Foreign Keys

Foreign keys are used to form an inextricable bond between tables. They always refer to the primary key of another table.

Normalization

Much of database management is akin to separating wheat from the chaff. One of the processes that allow you to do so is normalization.

Purpose of Normalization

Normalization is about restoring (or creating) order in a database. It’s the procedure of eradicating unnecessary data for the purpose of cleaner tables and smoother management.

Normal Forms

Every action has its reaction. For example, the reaction of normalization is normal forms. These are forms of data that are free from redundant or duplicate information, making them easily accessible.

Popular RDBMS Software

This article has dissected basic relational database concepts, the RDBMS meaning, and RDBMS full form. To further shed light on the technology, take a look at the crème de la crème of RDBMS platforms.

Oracle Database

If you want to make headway in the database management industry, Oracle Database can be one of your best friends.

Overview of Oracle Database

Oracle Database is the most famous RDBMS around. The very database of this network is called Oracle, and the software comes in five different versions. Each rendition has a specific set of features and benefits, but some perks hold true for each one.

Key Features and Benefits

• Highly secure – Oracle employs top-grade security measures.
• Scalable – The system supports company growth with adaptable features.
• Available – You can tap into the architecture whenever necessary for seamless adjustments.

Microsoft SQL Server

Let’s see what another powerhouse – Microsoft SQL Server – brings to the table.

Overview of Microsoft SQL Server

Microsoft SQL Server is a reliable RDBMS with admirable capabilities. Like Oracle, it’s available in a range of editions to target different groups, including personal and enterprise users.

Key Features and Benefits

• Fast – Few systems rival the speed of Microsoft SQL Server.
• Versatile – The network supports on-premise and cloud applications.
• Affordable – You won’t burn a hole in your pocket if you buy the standard version.

MySQL

You can take your business to new heights with MySQL. The following section will explore what makes this RDBMS a go-to pick for Uber, Slack, and many other companies.

Overview of MySQL

MySQL is another robust RDBMS that enables fast data retrieval. It’s an open-source solution, making it less complex than some other platforms.

Key Features and Benefits

• Quick – Efficient memory use speeds up the MySQL environment.
• Secure – Bulletproof password systems safeguard against hacks.
• Scalable – You can use MySQL both for small and large data sets.

PostgreSQL

Last but not least, PostgreSQL is a worthy contender for the best RDBMS on the market.

Overview of PostgreSQL

If you need a long-running RDBMS, you can’t go wrong with PostgreSQL. It’s an open-source solution that’s received more than two decades’ worth of refinement.

Key Features and Benefits

• Nested transactions – These elements deliver higher concurrency control.
• Anti-hack environment – Advanced locking features keep cybercriminals at bay.
• Table inheritance – This feature makes the network more consistent.

RDBMS Use Cases

Now we get to what might be the crux of the RDBMS discussion: Where can you implement these convenient solutions?

Data Storage and Retrieval

• Storing large amounts of structured data – Use an RDBMS to keep practically unlimited structured data.
• Efficient data retrieval – Retrieve data in a split second with an RDBMS.

Data Analysis and Reporting

• Analyzing data for trends and patterns – Discover customer behavior trends with a robust RDBMS.
• Generating reports for decision-making – Facilitate smart decision-making with RDBMS-generated reports.

Application Development

• Backend for web and mobile applications – Develop a steady web and mobile backend architecture with your RDBMS.
• Integration with other software and services – Combine an RDBMS with other programs to elevate its functionality.

RDBMS vs. NoSQL Database

Many alternatives to RDBMS have sprung up, including NoSQL databases. But what makes these two systems different?

Overview of NoSQL Databases

A NoSQL database is the stark opposite of RDBMS solutions. It takes a non-relational approach, which is deemed more efficient by many.

Key Differences Between RDBMS and NoSQL Databases

• Data model – RDBMSs store structured data, whereas NoSQL databases store unstructured information.
• Scalability – NoSQL is more scalable because it doesn’t require a fixed schema (relation-based model).
• Consistency – RDBMSs achieve consistency through rules, while NoSQL models feature eventual consistency.

Choosing the Right Database for Your Needs

Keep these guidelines in mind when selecting your database platform:

• Use an RDBMS for centralized apps and NoSQL for decentralized solutions.
• Use an RDBMS for structured data and NoSQL for unstructured data.
• Use an RDBMS for moderate data activity and NoSQL for high data activity.

Exploring the Vast Utility of RDBMS

If you’re looking for a descriptive answer to the “what is relational database management system question,” here it is – it is the cornerstone of database management for countless enterprises. It’s ideal for structured data projects and gives the user the reins of data management. Plus, it’s as secure as it gets.

The future looks even more promising. Database professionals are expected to rely more on blockchain technology and cloud storage to elevate the efficacy of RDBMS.

An ER diagram in DBMS (database management systems) is a lot like a storyboard for an animated TV show – it’s a collection of diagrams that show how everything fits together. Where a storyboard demonstrates the flow from one scene to the next, an ER diagram highlights the components of your databases and the relationships they share.

Understanding the ER model in DBMS is the first step to getting to grips with basic database software (like Microsoft Access) and more complex database-centric programming languages, such as SQL. This article explores ER diagrams in detail.

ER Model in DBMS

An ER diagram in DBMS is a tangible representation of the tables in a database, the relationships between each of those tables, and the attributes of each table. These diagrams feature three core components:

• Entities – Represented by rectangles in the diagram, entities are objects or concepts used throughout your database.
• Attributes – These are the properties that each entity possesses. ER diagrams use ellipses to represent attributes, with the attributes themselves tending to be the fields in a table. For example, an entity for students in a school’s internal database may have attributes for student names, birthdays, and unique identification numbers.
• Relationships – No entity in an ER diagram is an island, as each is linked to at least one other. These relationships can take multiple forms, with said relationships dictating the flow of information through the database.

Mapping out your proposed database using the ER model is essential because it gives you a visual representation of how the database works before you start coding or creating. Think of it like the blueprint you’d use to build a house, with that blueprint telling you where you need to lay every brick and fit every door.

Entities in DBMS

An Entity in DBMS tends to represent a real-life thing (like the students mentioned previously) that you can identify with certain types of data. Each entity is distinguishable from the others in your database, meaning you won’t have multiple entities listing student details.

Entities come in two flavors:

• Tangible Entities – These are physical things that exist in the real world, such as a person, vehicle, or building.
• Intangible Entities – If you can see and feel an entity, it’s intangible. Bank accounts are good examples. We know they exist (and have data attributed to them) but we can’t physically touch them.

There are also different entity strengths to consider:

• Strong Entities – A strong entity is represented using a rectangle and will have at least one key attribute attached to it that allows you to identify it uniquely. In the student example we’ve already shared, a student’s ID number could be a unique identifier, creating a key attribute that leads to the “Student” entity being strong.
• Weak Entities – Weak entities have no unique identifiers, meaning you can’t use them alone. Represented using double-outlined rectangles, these entities rely on the existence of strong entities to exist themselves. Think of it like the relationship between parent and child. A child can’t exist without a parent, in the same way that a weak entity can’t exist without a strong entity.

Once you’ve established what your entities are, you’ll gather each specific type of entity into an entity set. This set is like a table that contains the data for each entity in a uniform manner. Returning to the student example, any entity that has a student ID number, name, and birthdate, may be placed into an overarching “Student” entity set. They’re basically containers for specific entity types.

Attributes in DBMS

Every entity you establish has attributes attached to it, as you’ve already seen with the student example used previously. These attributes offer details about various aspects of the entity and come in four types:

• Simple Attributes – A simple attribute is any attribute that you can’t break down into further categories. A student ID number is a good example, as this isn’t something you can expand upon.
• Composite Attributes – Composite attributes are those that may have other attributes attached to them. If “Name” is one of your attributes, its composites could be “First Name,” “Surname,” “Maiden Name,” and “Nickname.”
• Derived Attributes – If you can derive an attribute from another attribute, it falls into this category. For instance, you can use a student’s date of birth to derive their age and grade level. These attributes have dotted ellipses surrounding them.
• Multi-valued Attributes – Represented by dual-ellipses, these attributes cover anything that can have multiple values. Phone numbers are good examples, as people can have several cell phone or landline numbers.

Attributes are important when creating an ER model in DBMS because they show you what types of data you’ll use to populate your entities.

Relationships in DBMS

As your database becomes more complex, you’ll create several entities and entity sets, with each having relationships with others. You represent these relationships using lines, creating a network of entities with line-based descriptions telling you how information flows between them.

There are three types of relationships for an ER diagram in DBMS:

• One-to-One Relationships – You’ll use this relationship when one entity can only have one of another entity. For example, if a school issues ID cards to its students, it’s likely that each student can only have one card. Thus, you have a one-to-one relationship between the student and ID card entities.
• One-to-Many Relationships – This relationship type is for when one entity can have several of another entity, but the relationship doesn’t work in reverse. Bank accounts are a good example, as a customer can have several bank accounts, but each account is only accessible to one customer.
• Many-to-Many Relationships – You use these relationships to denote when two entities can have several of each other. Returning to the student example, a student will have multiple classes, with each class containing several students, creating a many-to-many relationship.

These relationships are further broken down into “relationship sets,” which bring together all of the entities that participate in the same type of relationship. These sets have three varieties:

• Unary – Only one entity participates in the relationship.
• Binary – Two entities are in the relationship, such as the student and course example mentioned earlier.
• n-ary – Multiple entities participate in the relationship, with “n” being the number of entities.

Your ER diagram in DBMS needs relationships to show how each entity set relates to (and interacts with) the others in your diagram.

ER Diagram Notations

You’ll use various forms of notation to denote the entities, attributes, relationships, and the cardinality of those relationships in your ER diagram.

Entity Notations

Entities are denoted using rectangles around a word or phrase, with a solid rectangle meaning a strong entity and a double-outlined rectangle denoting a weak entity.

Attribute Notations

Ellipses are the shapes of choice for attributes, with the following uses for each attribute type:

• Simple and Composite Attribute – Solid line ellipses
• Derived Attribute – Dotted line ellipses
• Multi-Valued Attribute – Double-lined ellipses

Relationship Notations

Relationship notation uses diamonds, with a solid line diamond depicting a relationship between two attributes. You may also find double-lined diamonds, which signify the relationship between a weak entity and the strong entity that owns it.

Cardinality and Modality Notations

These lines show you the maximum times an instance in one entity set can relate to the instances of another set, making them crucial for denoting the relationships inside your database.

The endpoint of the line tells you everything you need to know about cardinality and ordinality. For example, a line that ends with three lines (two going diagonally) signifies a “many” cardinality, while a line that concludes with a small vertical line signifies a “one” cardinality. Modality comes into play if there’s a minimum number of instances for an entity type. For example, a person can have many phone numbers but must have at least one.

Steps to Create an ER Diagram in DBMS

With the various notations for an ER diagram in DBMS explained, you can follow these steps to draw your own diagram:

• Identify Entities – Every tangible and intangible object that relates to your database is an entity that you need to identify and define.
• Identify Attributes – Each entity has a set of attributes (students have names, ID numbers, birthdates, etc.) that you must define.
• Identify Relationships – Ask yourself how each entity set fits together to identify the relationships that exist between them.
• Assign Cardinality and Modality – If you have an instance from Entity A, how many instances does it relate to in Entity B? Is there a minimum to consider? Assign cardinalities and modalities to offer the answers.
• Finalize Your Diagram – Take a final pass over the diagram to ensure all required entities are present, they have the appropriate attributes, and that all relationships are defined.

Examples of ER Diagrams in DBMS

Once you understand the basics of the ER model in DBMS, you’ll see how they can apply to multiple scenarios:

• University Databases – A university database will have entities such as “Student,” “Teacher,” “Course,” and “Class.” Attributes depend on the entity, with the people-based entities having attributes including names, dates of birth, and ID numbers. Relationships vary (i.e., a student may only have one teacher but a single teacher may have several students).
• Hospital Management Databases – Entities for this type of database include people (“Patients,” “Doctors,” and “Nurses”), as well as other tangibles, such as different hospital buildings and inventory. These databases can get very complex, with multiple relationships linking the various people involved to different buildings, treatment areas, and inventory.
• E-Commerce Databases – People play an important role in the entities for e-commerce sites, too, because every site needs a list of customers. Those customers have payment details and order histories, which are potential entities or attributes. Product lists and available inventory are also factors.

Master the ER Model in DBMS

An ER diagram in DBMS can look like a complicated mass of shapes and lines at first, making them feel impenetrable to those new to databases. But once you get to grips with what each type of shape and line represents, they become crucial tools to help you outline your databases before you start developing them.

Application of what you’ve learned is the key to success with ER diagrams (and any other topic), so take what you’ve learned here and start experimenting. Consider real-world scenarios (such as those introduced above) and draw diagrams based on the entities you believe apply to those scenarios. Build up from there to figure out the attributes and relationships between entity sets and you’re well on your way to a good ER diagram.

The larger your database, the higher the possibility of data repetition and inaccuracies that compromise the results you pull from the database. Normalization in DBMS exists to counteract those problems by helping you to create more uniform databases in which redundancies are less likely to occur.

Mastering normalization is a key skill in DBMS for the simple fact that an error-strewn database is of no use to an organization. For example, a retailer that has to deal with a database that has multiple entries for phone numbers and email addresses is a retailer that can’t see as effectively as one that has a simple route to the customer. Let’s look at normalization in DBMS and how it helps you to create a more organized database.

The Concept of Normalization

Grab a pack of playing cards and throw them onto the floor. Now, pick up the “Jack of Hearts.” It’s a tough task because the cards are strewn all over the place. Some are facing down and there’s no rhyme, reason, or pattern to how the cards lie, meaning you’re going to have to check every card individually to find the one you want.

That little experiment shows you how critical organization is, even with a small set of “data.” It also highlights the importance of normalization in DBMS. Through normalization, you implement organizational controls using a set of principles designed to achieve the following:

• Eliminate redundancy – Lower (or eliminate) occurrences of data repeating across different tables, or inside individual tables, in your DBMS.
• Minimize data anomalies – Better organization makes it easier to spot datasets that don’t fit the “norm,” meaning fewer anomalies.
• Improve data integrity – More accurate data comes from normalization controls. Database users can feel more confident in their results because they know that the controls ensure integrity.

The Process of Normalization

If normalization in DBMS is all about organization, it stands to reason that they would be a set process to follow when normalizing your tables and database:

1. Decompose your tables – Break every table down into its various parts, which may lead to you creating several tables out of one. Through decomposition, you separate different datasets, eliminate inconsistencies, and set the stage for creating relationships and dependencies between tables.
2. Identify functional dependencies – An attribute in one table may be dependent on another to exist. For example, a “Customer ID” number in a retailer’s “Customer” table is functionally dependent on the “Customer Name” field because the ID can’t exist without the customer. Identifying these types of dependencies ensures you don’t end up with empty records (such as a record with a “Customer ID” and no customer attached to it).
3. Apply normalization rules – Once you’re broken down your table and identified the functional dependencies, you apply relevant normalization rules. You’ll use Normal Forms to do this, with the six highlighted below each having its own rules, structures, and use cases.

Normal Forms in DBMS

There isn’t a “single” way to achieve normalization in DBMS because every database (and the tables it contains) is different. Instead, there are six normal forms you may use, with each having its own rules that you need to understand to figure out which to apply.

First Normal Form (1NF)

If a relation can’t contain multiple values, it’s in 1NF. In other words, each attribute in the table can only contain a single (called “atomic”) value.

Example

If a retailer wants to store the details of its customers, it may have attributes in its table like “Customer Name,” “Phone Number,” and “Email Address.” By applying 1NF to this table, you ensure that the attributes that could contain multiple entries (“Phone Number” and “Email Address”) only contain one, making contacting that customer much simpler.

Second Normal Form (2NF)

A table that’s in 2NF is in 1NF, with the additional condition that none of its non-prime attributes depend on a subset of candidate keys within the table.

Example

Let’s say an employer wants to create a table that contains information about an employee, the skills they have, and their age. An employee may have multiple skills, leading to multiple records for the same employee in the table, with each denoting a skill while the ID number and age of the employee repeat for each record.

In this table, you’ve achieved 1NF because each attribute has an atomic value. However, the employee’s age is dependent on the employee ID number. To achieve 2NF, you’d break this table down into two tables. The first will contain the employee’s ID number and age, with that ID number linking to a second table that lists each of the skills associated with the employee.

Third Normal Form (3NF)

In 3NF, the table you have must already be in 2NF form, with the added rule of removing the transitive functional dependency of the non-prime attribute of any super key. Transitive functional dependency occurs if the dependency is the result of a pair of functional dependencies. For example, the relationship between A and C is a transitive dependency if A depends on B, B depends on C, but B doesn’t depend on A.

Example

Let’s say a school creates a “Students” table with the following attributes:

• Student ID
• Name
• Zip Code
• State
• City
• District

In this case, the “State,” “District,” and “City” attributes all depend on the “Zip Code” attribute. That “Zip” attribute depends on the “Student ID” attribute, making “State,” “District,” and “City” all transitively depending on “Student ID.”

To resolve this problem, you’d create a pair of tables – “Student” and “Student Zip.” The “Student” table contains the “Student ID,” “Name,” and “Zip Code” attributes, with that “Zip Code” attribute being the primary key of a “Student Zip” table that contains the rest of the attributes and links to the “Student” table.

Boyce-Codd Normal Form (BCNF)

Often referred to as 3.5NF, BCNF is a stricter version of 3NF. So, this normalization in DBMS rule occurs if your table is in 3NF, and for every functional dependence between two fields (i.e., A -> B), A is the super key of your table.

Example

Sticking with the school example, every student in a school has multiple classes. The school has a table with the following fields:

• Student ID
• Nationality
• Class
• Class Type
• Number of Students in Class

You have several functional dependencies here:

• Student ID -> Nationality
• Class -> Number of Students in Class, Class Type

As a result, both the “Student ID” and “Class” attributes are candidate keys but can’t serve as keys alone. To achieve BCNF normalization, you’d break the above table into three – “Student Nationality,” “Student Class,” and “Class Mapping,” allowing “Student ID” and “Class” to serve as primary keys in their own tables.

Fourth Normal Form (4NF)

In 4NF, the database must meet the requirements of BCNF, in addition to containing no more than a single multivalued dependency. It’s often used in academic circles, as there’s little use for 4NF elsewhere.

Example

Let’s say a college has a table containing the following fields:

• College Course
• Lecturer
• Recommended Book

Each of these attributes is independent of the others, meaning each can change without affecting the others. For example, the college could change the lecturer of a course without altering the recommended reading or the course’s name. As such, the existence of the course depends on both the “Lecturer” and “Recommended Book” attributes, creating a multivalued dependency. If a DBMS has more than one of these types of dependencies, it’s a candidate for 4NF normalization.

Fifth Normal Form (5NF)

If your table is in 4NF, has no join dependencies, and all joining is lossless, it’s in 5NF. Think of this as the final form when it comes to normalization in DBMS, as you’ve broken your table down so much that you’ve made redundancy impossible.

Example

A college may have a table that tells them which lecturers teach certain subjects during which semesters, creating the following attributes:

• Subject
• Lecturer Name
• Semester

Let’s say one of the lecturers teaches both “Physics” and “Math” for “Semester 1,” but doesn’t teach “Math” for Semester 2. That means you need to combine all of the fields in this table to get an accurate dataset, leading to redundancy. Add a third semester to the mix, especially if that semester has no defined courses or lecturers, and you have to join dependencies.

The 5NF solution is to break this table down into three tables:

• Table 1 – Contains the “Semester” and “Subject” attributes to show which subjects are taught in each semester.
• Table 2 – Contains the “Subject” and “Lecturer Name” attributes to show which lecturers teach a subject.
• Table 3 – Contains the “Semester” and “Lecturer Name” attributes so you can see which lecturers teach during which semesters.

Benefits of Normalization in DBMS

With normalization in DBMS being so much work, you need to know the following benefits to show that it’s worth your effort:

• Improved database efficiency
• Better data consistency
• Easier database maintenance
• Simpler query processing
• Better access controls, resulting in superior security

Limitations and Trade-Offs of Normalization

Normalization in DBMS does have some drawbacks, though these are trade-offs that you accept for the above benefits:

• The larger your database gets, the more demands it places on system performance.
• Breaking tables down leads to complexity.
• You have to find a balance between normalization and denormalization to ensure your tables make sense.

Practical Tips for Mastering Normalization Techniques

Getting normalization in DBMS is hard, especially when you start feeling like you’re dividing tables into so many small tables that you’re losing track of the database. These tips help you apply normalization correctly:

• Understand the database requirements – Your database exists for you to extract data from it, so knowing what you’ll need to extract indicates whether you need to normalize tables or not.
• Document all functional dependencies – Every functional dependence that exists in your database makes the table in which it exists a candidate for normalization. Identify each dependency and document it so you know whether you need to break the table down.
• Use software and tools – You’re not alone when poring through your database. There are plenty of tools available that help you to identify functional dependencies. Many make normalization suggestions, with some even being able to carry out those suggestions for you.
• Review and refine – Every database evolves alongside its users, so continued refining is needed to identify new functional dependencies (and opportunities for normalization).
• Collaborate with other professionals – A different set of eyes on a database may reveal dependencies and normalization opportunities that you don’t see.

Make Normalization Your New Norm

Normalization may seem needlessly complex, but it serves the crucial role of making the data you extract from your database more refined, accurate, and free of repetition. Mastering normalization in DBMS puts you in the perfect position to create the complex databases many organizations need in a Big Data world. Experiment with the different “normal forms” described in this article as each application of the techniques (even for simple tables) helps you get to grips with normalization.