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!
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From the same author
Read the articleAs one of the world’s fastest-growing industries, with a predicted compound annual growth rate of 16.43% anticipated between 2022 and 2030, data science is the ideal choice for your career. Jobs will be plentiful. Opportunities for career advancement will come thick and fast. And even at the most junior level, you’ll enjoy a salary that comfortably sits in the mid-five figures.
Studying for a career in this field involves learning the basics (and then the complexities) of programming languages including C+, Java, and Python. The latter is particularly important, both due to its popularity among programmers and the versatility that Python brings to the table. Here, we explore the importance of Python for data science and how you’re likely to use it in the real world.
Why Python for Data Science?
We can distill the reasons for learning Python for data science into the following five benefits.
Popularity and Community Support
Statista’s survey of the most widely-used programming languages in 2022 tells us that 48.07% of programmers use Python to some degree. Leftronic digs deeper into those numbers, telling us that there are 8.2 million Python developers in the world. As a prospective developer yourself, these numbers tell you two things – Python is in demand and there’s a huge community of fellow developers who can support you as you build your skills.
Easy to Learn and Use
You can think of Python as a primer for almost any other programming language, as it takes the fundamental concepts of programming and turns them into something practical. Getting to grips with concepts like functions and variables is simpler in Python than in many other languages. Python eventually opens up from its simplistic use cases to demonstrate enough complexity for use in many areas of data science.
Extensive Libraries and Tools
Given that Python was first introduced in 1991, it has over 30 years of support behind it. That, combined with its continued popularity, means that novice programmers can access a huge number of tools and libraries for their work. Libraries are especially important, as they act like repositories of functions and modules that save time by allowing you to benefit from other people’s work.
Integration With Other Programming Languages
The entire script for Python is written in C, meaning support for C is built into the language. While that enables easy integration between these particular languages, solutions exist to link Python with the likes of C++ and Java, with Python often being capable of serving as the “glue” that binds different languages together.
Versatility and Flexibility
If you can think it, you can usually do it in Python. Its clever modular structure, which allows you to define functions, modules, and entire scripts in different files to call as needed, makes Python one of the most flexible programming languages around.
Setting Up Python for Data Science
Installing Python onto your system of choice is simple enough. You can download the language from the Python.org website, with options available for everything from major operating systems (Windows, macOS, and Linux) to more obscure devices.
However, you need an integrated development environment (IDE) installed to start coding in Python. The following are three IDEs that are popular with those who use Python for data science:
- Jupyter Notebook – As a web-based application, Jupyter easily allows you to code, configure your workflows, and even access various libraries that can enhance your Python code. Think of it like a one-stop shop for your Python needs, with extensions being available to extend its functionality. It’s also free, which is never a bad thing.
- PyCharm – Where Jupyter is an open-source IDE for several languages, PyCharm is for Python only. Beyond serving as a coding tool, it offers automated code checking and completion, allowing you to quickly catch errors and write common code.
- Visual Studio Code – Though Visual Studio Code alone isn’t compatible with Python, it has an extension that allows you to edit Python code on any operating system. Its “Linting” feature is great for catching errors in your code, and it comes with an integrated debugger that allows you to test executables without physically running them.
Setting up your Python virtual environment is as simple as downloading and installing Python itself, and then choosing an IDE in which to work. Think of Python as the materials you use to build a house, with your IDE being both the blueprint and the tools you’ll need to patch those materials together.
Essential Python Libraries for Data Science
Just as you’ll go to a real-world library to check out books, you can use Python libraries to “check out” code that you can use in your own programs. It’s actually better than that because you don’t need to return libraries when you’re done with them. You get to keep them, along with all of their built-in modules and functions, to call upon whenever you need them. In Python for data science, the following are some essential libraries:
- NumPy – We spoke about integration earlier, and NumPy is ideal for that. It brings concepts of functionality from Fortran and C into Python. By expanding Python with powerful array and numerical computing tools, it helps transform it into a data science powerhouse.
- pandas – Manipulating and analyzing data lies at the heart of data sciences, and pandas give you a library full of tools to allow both. It offers modules for cleaning data, plotting, finding correlations, and simply reading CSV and JSON files.
- Matplotlib – Some people can look at reams of data and see patterns form within the numbers. Others need visualization tools, which is where Matplotlib excels. It helps you create interactive visual representations of your data for use in presentations or if you simply prefer to “see” your data rather than read it.
- Scikit-learn – The emerging (some would say “exploding) field of machine learning is critical to the AI-driven future we’re seemingly heading toward. Scikit-learn is a library that offers tools for predictive data analysis, built on what’s available in the NumPy and Matplotlib libraries.
- TensorFlow and Keras – Much like Scikit-learn, both TensorFlow and Keras offer rich libraries of tools related to machine learning. They’re essential if your data science projects take you into the realms of neural networks and deep learning.
Data Science Workflow in Python
A Python programmer without a workflow is like a ship’s captain without a compass. You can sail blindly onward, and you may even get lucky and reach your destination, but the odds are you’re going to get lost in the vastness of the programming sea. For those who want to use Python for data science, the following workflow brings structure and direction to your efforts.
Step 1 – Data Collection and Preprocessing
You need to collect, organize, and import your data into Python (as well as clean it) before you can draw any conclusions from it. That’s why the first step in any data science workflow is to prepare the data for use (hint – the pandas library is perfect for this task).
Step 2 – Exploratory Data Analysis (EDA)
Just because you have clean data, that doesn’t mean you’re ready to investigate what that data tells you. It’s like washing ingredients before you make a dish – you need to have a “recipe” that tells you how to put everything together. Data scientists use EDA as this recipe, allowing them to combine data visualization (remember – the Matplotlib library) with descriptive statistics that show them what they’re looking at.
Step 3 – Feature Engineering
This is where you dig into the “whats” and “hows” of your Python program. You’ll select features for the code, which define what it does with the data you import and how it’ll deliver outcomes. Scaling is a key part of this process, with scope creep (i.e., constantly adding features as you get deeper into a project) being the key thing to avoid.
Step 4 – Model Selection and Training
Decision trees, linear regression, logistic regression, neural networks, and support vector machines. These are all models (with their own algorithms) you can use for your data science project. This step is all about selecting the right model for the job (your intended features are important here) and training that model so it produces accurate outputs.
Step 5 – Model Evaluation and Optimization
Like a puppy that hasn’t been house trained, an unevaluated model isn’t ready for release into the real world. Classification metrics, such as a confusion matrix and classification report, help you to evaluate your model’s predictions against real-world results. You also need to tune the hyperparameters built into your model, similar to how a mechanic may tune the nuts and bolts in a car, to get everything working as efficiently as possible.
Step 6 – Deployment and Maintenance
You’ve officially deployed your Python for data science model when you release it into the wild and let it start predicting outcomes. But the work doesn’t end at deployment, as constant monitoring of what your model does, outputs, and predicts is needed to tell you if you need to make tweaks or if the model is going off the rails.
Real-World Data Science Projects in Python
There are many examples of Python for data science in the real world, some of which are simple while others delve into some pretty complex datasets. For instance, you can use a simple Python program to scrap live stock prices from a source like Yahoo! Finance, allowing you to create a virtual ticker of stock price changes for investors.
Alternatively, why not create a chatbot that uses natural language processing to classify and respond to text? For that project, you’ll tokenize sentences, essentially breaking them down into constituent words called “tokens,” and tag those tokens with meanings that you could use to prompt your program toward specific responses.
There are plenty of ideas to play around with, and Python is versatile enough to enable most, so consider what you’d like to do with your program and then go on the hunt for datasets. Great (and free) resources include The Boston House Price Dataset, ImageNet, and IMDB’s movie review database.
Try Python for Data Science Projects
By combining its own versatility with integrations and an ease of use that makes it welcoming to beginners, Python has become one of the world’s most popular programming languages. In this introduction to data science in Python, you’ve discovered some of the libraries that can help you to apply Python for data science. Plus, you have a workflow that lends structure to your efforts, as well as some ideas for projects to try. Experiment, play, and tweak models. Every minute you spend applying Python to data science is a minute spent learning a popular programming language in the context of a rapidly-growing industry.
Read the articleToday’s tech-driven world is governed by data – so much so that nearly 98% of all organizations are increasing investment in data.
However, company owners can’t put their feet up after improving their data capabilities. They also need a database management system (DBMS) – a program specifically designed for storing and organizing information efficiently.
When analyzing a DBMS, you need to be thorough like a detective investigating a crime. One of the elements you want to consider is DBMS architecture. It describes the structure of your database and how individual bits of information are related to each other. The importance of DBMS architecture is enormous, as it helps IT experts design and maintain fully functional databases.
But what exactly does a DBMS architecture involve? You’ll find out in this entry. Coming up is an in-depth discussion of database system concepts and architecture.
Overview of DBMS Architecture
Suppose you’re assembling your PC. You can opt for several configurations, such as those with three RAM slots and dual-fan coolers. The same principle applies to DBMS architectures.
Two of the most common architectures are three-level and two-level architectures.
Three-Level Architecture
Three-level architecture is like teacher-parent communication. More often than not, a teacher communicates with parents through children, asking them to convey certain information. In other words, there are layers between the two that don’t allow direct communication.
The same holds for three-level architecture. But instead of just one layer, there are two layers between the database and user: application client and application server.
And as the name suggests, a three-level DBMS architecture has three levels:
- External level – Also known as the view level, this section concerns the part of your database that’s relevant to the user. Everything else is hidden.
- Conceptual level – Put yourself in the position of a scuba diver exploring the ocean layer by layer. Once you reach the external level, you go one segment lower and find the conceptual level. It describes information conceptually and tells you how data segments interact with one another.
- Internal level – Another name for the internal level is the physical level. But what does it deal with? It mainly focuses on how data is stored in your system (e.g., using folders and files).
Two-Level Architecture
When you insert a USB into your PC, you can see the information on your interface. However, the source of the data is on the USB, meaning they’re separated.
Two-level architecture takes the same approach to separating data interface and data structure. Here are the two levels in this DBMS architecture:
- User level – Any application and interface in your database are stored on the user level in a two-level DBMS architecture.
- System level – The system level (aka server level) performs transaction management and other essential processes.
Comparison of the Two Architectures
Determining which architecture works best for your database is like buying a car. You need to consider how easy it is to use and the level of performance you can expect.
On the one hand, the biggest advantage of two-level architectures is that they’re relatively easy to set up. There’s just one layer between the database and the user, resulting in easier database management.
On the other hand, developing a three-level DBMS architecture may take a while since you need to include two layers between the database and the user. That said, three-level architectures are normally superior to two-level architectures due to higher flexibility and the ability to incorporate information from various sources.
Components of DBMS Architecture
You’ve scratched the surface of database system concepts and architecture, but don’t stop there. It’s time to move on to the basics to the most important elements of a DBMS architecture:
Data Storage
The fact that DBMS architectures have data storage solutions is carved in stone. What exactly are those solutions? The most common ones are as follows:
- Data files – How many files do you have on your PC? If it’s a lot, you’re doing exactly what administrators of DBMS architectures are doing. A large number of them store data in files, and each file is categorized into blocks.
- Indexes – You want your database operations to be like lightning bolts, i.e. super-fast. You can incorporate indexes to accomplish this goal. They point to data columns for quick retrieval.
- Data dictionary – Also known as system logs, data dictionaries contain metadata – information about your data.
Data Manipulation
A large number of companies still utilize manual data management methods. But using this format is like shooting yourself in the foot when there are advanced data manipulation methods are available. These allow you to process and retrieve data within seconds through different techniques:
- Query processor – Query processing refers to extracting data from your DBMS architecture. It operates like any other multi-stage process. It involves parsing, translation, optimization, and evaluation.
- Query optimizer – A DBMS architecture administrator can perform various query optimization tasks to achieve desired results faster.
- Execution engine – Whenever you want your architecture to do something, you send requests. But something needs to process the requests – that something is the execution engine.
Data Control
We’re continuing our journey through an average DBMS architecture. Our next stop is data control, which is comprised of these key elements:
- Transaction management – When carrying out multiple transactions, how does the system prioritize one over another? The answer lies in transaction management, which is also about processing multiple transactions side by side.
- Concurrency control – Database architecture is like an ocean teeming with life. Countless operations take place simultaneously. As a result, the system needs concurrency control to manage these concurrent tasks.
- Recovery management – What if your DBMS architecture fails? Do you give up on your project? No – the system has robust recovery management tools to retrieve your information and reduce downtime.
Database System Concepts
To give you a better understanding of a DBMS architecture, let’s describe the most important concepts regarding this topic.
Data Models
Data models do to information what your folders do to files – organize them. There are four major types of data models:
- Hierarchical model – Top-down and bottom-up storage solutions are known as hierarchical models. They’re characterized by tree-like structures.
- Network model – Hierarchical models are generally used for basic data relationships. If you want to analyze complex relationships, you need to kick things up a notch with network models. They enable you to represent huge quantities of complex information without a hitch.
- Relational model – Relations are merely tables with values. A relational model is a collection of these relations, indicating how data is connected to other data.
- Object-oriented model – Programming languages regularly use objects. An object-oriented model stores information as models and is usually more complex than other models.
Database Schema and Instances
Another concept you should familiarize yourself with is schemas and instances.
- Definition of schema and instance – Schemas are like summaries, providing a basic description of databases. Instances tell you what information is stored in a database.
- Importance of schema in DBMS architecture – Schemas are essential because they help organize data by providing a clear outline.
Data Independence
The ability of other pieces of information to remain unaffected after you change one bit of data is known as data independence. What are the different types of data independence, and what makes them so important?
- Logical data independence – If you can modify logical schemas without altering the rest of the system, your logical data is independent.
- Physical data independence – Physical data is independent if it remains unaffected when changing your hardware, such as SSD disks.
- Significance of data independence in DBMS architecture – Independent data is crucial for saving time in database management because it reduces the amount of information that needs to be processed.
Efficient Database Management Systems
Database management systems have a lot in common with other tech-based systems. For example, you won’t ignore problems that arise on your PC, be they CPU or graphics card issues. You’ll take action to optimize the performance of the device and solve those issues.
That’s exactly what 75% of developers and administrators of database management systems do. They go the extra mile to enhance the performance, scalability, flexibility, security, and integrity of their architecture.
Performance Optimization Techniques
- Indexing – By pointing to certain data in tables, indexes speed up database management.
- Query optimization – This process is about finding the most efficient method of executing queries.
- Caching – Frequently accessed information is cached to accelerate retrieval.
Scalability and Flexibility
- Horizontal scaling – Horizontal scaling involves increasing the number of servers.
- Vertical scaling – An administrator can boost the performance of the server to make the system more scalable.
- Distributed databases – Databases are like smartphones in that they can easily overload. Pressure can be alleviated with distributed databases, which store information in multiple locations.
Security and Integrity
- Access control – Restricting access is key to preventing cyber security attacks.
- Data encryption – Administrators often encrypt their DBMS architecture to protect sensitive information.
- Backup and recovery – A robust backup plan helps IT experts recover from shutdowns and other unforeseen problems.
Preparing for the Future Is Critical
DBMS architecture is the underlying structure of a database management system. It consists of several elements, all of which work together to create a fully functional data infrastructure.
Understanding the basic elements of DBMS architecture is vital for IT professionals who want to be well-prepared for future changes, such as hybrid environments. As the old saying goes – success depends upon preparation.
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Source:
- EFMD Global, Published on July 12th, 2024.
By Stephanie Mullins
Many people love to read the stories of successful business school graduates to see what they’ve achieved using the lessons, insights and connections from the programmes they’ve studied. We speak to one alumnus, Riccardo Ocleppo, who studied at top business schools including London Business School (LBS) and INSEAD, about the education institution called OPIT which he created after business school.
Please introduce yourself and your career to date.
I am the founder of OPIT — Open Institute of Technology, a fully accredited Higher Education Institution (HEI) under the European Qualification Framework (EQF) by the MFHEA Authority. OPIT also partners with WES (World Education Services), a trusted non-profit providing verified education credential assessments (ECA) in the US and Canada for foreign degrees and certificates.
Prior to founding OPIT, I established Docsity, a global community boasting 15 million registered university students worldwide and partnerships with over 250 Universities and Business Schools. My academic background includes an MSc in Electronics from Politecnico di Torino and an MSc in Management from London Business School.
Why did you decide to create OPIT Open Institute of Technology?
Higher education has a profound impact on people’s futures. Through quality higher education, people can aspire to a better and more fulfilling future.
The mission behind OPIT is to democratise access to high-quality higher education in the fields that will be in high demand in the coming decades: Computer Science, Artificial Intelligence, Data Science, Cybersecurity, and Digital Innovation.
Since launching my first company in the education field, I’ve engaged with countless students, partnered with hundreds of universities, and collaborated with professors and companies. Through these interactions, I’ve observed a gap between traditional university curricula and the skills demanded by today’s job market, particularly in Computer Science and Technology.
I founded OPIT to bridge this gap by modernising education, making it affordable, and enhancing the digital learning experience. By collaborating with international professors and forging solid relationships with global companies, we are creating a dynamic online community and developing high-quality digital learning content. This approach ensures our students benefit from a flexible, cutting-edge, and stress-free learning environment.
Why do you think an education in tech is relevant in today’s business landscape?
As depicted by the World Economic Forum’s “Future of Jobs 2023” report, the demand for skilled tech professionals remains (and will remain) robust across industries, driven by the critical role of advanced technologies in business success.
Today’s companies require individuals who can innovate and execute complex solutions. A degree in fields like computer science, cybersecurity, data science, digital business or AI equips graduates with essential skills to thrive in this dynamic industry.
According to the International Monetary Fund (IMF), the global tech talent shortage will exceed 85 million workers by 2030. The Korn Ferry Institute warns that this gap could result in hundreds of billions in lost revenue across the US, Europe, and Asia.
To address this challenge, OPIT aims to democratise access to technology education. Our competency-based and applied approach, coupled with a flexible online learning experience, empowers students to progress at their own pace, demonstrating their skills as they advance.
Read the full article below:
Source:
- The European, Summer 2024 Edition, Page 24
With careful planning, ethical considerations, and ensuring human oversight is maintained, AI can have huge market research benefits, says Lorenzo Livi of the Open Institute of Technology.
By Lorenzo Livi
To market well, you need to get something interesting in front of those who are interested. That takes a lot of thinking, a lot of work, and a whole bunch of research. But what if the bulk of that thinking, work and research could be done for you? What would that mean for marketing as an industry, and market research specifically?
With the recent explosion of AI onto the world stage, big changes are coming in the marketing industry. But will AI be able to do market research as successfully? Simply, the answer is yes. A big, fat, resounding yes. In fact, AI has the potential to revolutionise market research.
Ensuring that people have a clear understanding of what exactly AI is is crucial, given its seismic effect on our world. Common questions that even occur amongst people at the forefront of marketing, such as, “Who invented AI?” or, “Where is the main AI system located?” highlight a widespread misunderstanding about the nature of AI.
As for the notion of a central “main thing” running AI, it’s essential to clarify that AI systems exist in various forms and locations. AI algorithms and models can run on individual computers, servers, or even specialized hardware designed for AI processing, commonly referred to as AI chips. These systems can be distributed across multiple locations, including data centres, cloud platforms, and edge devices. They can also be used anywhere, so long as you have a compatible device and an internet connection.
While the concept of AI may seem abstract or mysterious to some, it’s important to approach it with a clear understanding of its principles and applications. By promoting education and awareness about AI, we can dispel misconceptions and facilitate meaningful conversations about its role in society.
Read the full article below:
- The European, Pages 24 to 26.
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