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.

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Sage: The ethics of AI: how to ensure your firm is fair and transparent
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 7, 2025 3 min read

Source:


By Chris Torney

Artificial intelligence (AI) and machine learning have the potential to offer significant benefits and opportunities to businesses, from greater efficiency and productivity to transformational insights into customer behaviour and business performance. But it is vital that firms take into account a number of ethical considerations when incorporating this technology into their business operations. 

The adoption of AI is still in its infancy and, in many countries, there are few clear rules governing how companies should utilise the technology. However, experts say that firms of all sizes, from small and medium-sized businesses (SMBs) to international corporations, need to ensure their implementation of AI-based solutions is as fair and transparent as possible. Failure to do so can harm relationships with customers and employees, and risks causing serious reputational damage as well as loss of trust.

What are the main ethical considerations around AI?

According to Pierluigi Casale, professor in AI at the Open Institute of Technology, the adoption of AI brings serious ethical considerations that have the potential to affect employees, customers and suppliers. “Fairness, transparency, privacy, accountability, and workforce impact are at the core of these challenges,” Casale explains. “Bias remains one of AI’s biggest risks: models trained on historical data can reinforce discrimination, and this can influence hiring, lending and decision-making.”

Part of the problem, he adds, is that many AI systems operate as ‘black boxes’, which makes their decision-making process hard to understand or interpret. “Without clear explanations, customers may struggle to trust AI-driven services; for example, employees may feel unfairly assessed when AI is used for performance reviews.”

Casale points out that data privacy is another major concern. “AI relies on vast datasets, increasing the risk of breaches or misuse,” he says. “All companies operating in Europe must comply with regulations such as GDPR and the AI Act, ensuring responsible data handling to protect customers and employees.”

A third significant ethical consideration is the potential impact of AI and automation on current workforces. Businesses may need to think about their responsibilities in terms of employees who are displaced by technology, for example by introducing training programmes that will help them make the transition into new roles.

Olivia Gambelin, an AI ethicist and the founder of advisory network Ethical Intelligence, says the AI-related ethical considerations are likely to be specific to each business and the way it plans to use the technology. “It really does depend on the context,” she explains. “You’re not going to find a magical checklist of five things to consider on Google: you actually have to do the work, to understand what you are building.”

This means business leaders need to work out how their organisation’s use of AI is going to impact the people – the customers and employees – that come into contact with it, Gambelin says. “Being an AI-enabled company means nothing if your employees are unhappy and fearful of their jobs, and being an AI-enabled service provider means nothing if it’s not actually connecting with your customers.”

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Reuters: EFG Watch: DeepSeek poses deep questions about how AI will develop
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Feb 10, 2025 4 min read

Source:

  • Reuters, Published on February 10th, 2025.

By Mike Scott

Summary

  • DeepSeek challenges assumptions about AI market and raises new ESG and investment risks
  • Efficiency gains significant – similar results being achieved with less computing power
  • Disruption fuels doubts over Big Tech’s long-term AI leadership and market valuations
  • China’s lean AI model also casts doubt on costly U.S.-backed Stargate project
  • Analysts see DeepSeek as a counter to U.S. tariffs, intensifying geopolitical tensions

February 10 – The launch by Chinese company DeepSeek, opens new tab of its R1 reasoning model last month caused chaos in U.S. markets. At the same time, it shone a spotlight on a host of new risks and challenged market assumptions about how AI will develop.

The shock has since been overshadowed by President Trump’s tariff wars, opens new tab, but DeepSeek is set to have lasting and significant implications, observers say. It is also a timely reminder of why companies and investors need to consider ESG risks, and other factors such as geopolitics, in their investment strategies.

“The DeepSeek saga is a fascinating inflection point in AI’s trajectory, raising ESG questions that extend beyond energy and market concentration,” Peter Huang, co-founder of Openware AI, said in an emailed response to questions.

DeepSeek put the cat among the pigeons by announcing that it had developed its model for around $6 million, a thousandth of the cost of some other AI models, while also using far fewer chips and much less energy.

Camden Woollven, group head of AI product marketing at IT governance and compliance group GRC International, said in an email that “smaller companies and developers who couldn’t compete before can now get in the game …. It’s like we’re seeing a democratisation of AI development. And the efficiency gains are significant as they’re achieving similar results with much less computing power, which has huge implications for both costs and environmental impact.”

The impact on AI stocks and companies associated with the sector was severe. Chipmaker Nvidia lost almost $600 billion in market capitalisation after the DeepSeek announcement on fears that demand for its chips would be lower, but there was also a 20-30% drop in some energy stocks, said Stephen Deadman, UK associate partner at consultancy Sia.

As Reuters reported, power producers were among the biggest winners in the S&P 500 last year, buoyed by expectations of ballooning demand from data centres to scale artificial intelligence technologies, yet they saw the biggest-ever one-day drops after the DeepSeek announcement.

One reason for the massive sell-off was the timing – no-one was expecting such a breakthrough, nor for it to come from China. But DeepSeek also upended the prevailing narrative of how AI would develop, and who the winners would be.

Tom Vazdar, professor of cybersecurity and AI at Open Institute of Technology (OPIT), pointed out in an email that it called into question the premise behind the Stargate Project,, opens new tab a $500 billion joint venture by OpenAI, SoftBank and Oracle to build AI infrastructure in the U.S., which was announced with great fanfare by Donald Trump just days before DeepSeek’s announcement.

“Stargate has been premised on the notion that breakthroughs in AI require massive compute and expensive, proprietary infrastructure,” Vazdar said in an email.

There are also dangers in markets being dominated by such a small group of tech companies. As Abbie Llewellyn-Waters, Investment manager at Jupiter Asset Management, pointed out in a research note, the “Magnificent Seven” tech stocks had accounted for nearly 60% of the index’s gains over the previous two years. The group of mega-caps comprised more than a third of the S&P 500’s total value in December 2024.

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