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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Raconteur: AI on your terms – meet the enterprise-ready AI operating model
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Nov 18, 2025 5 min read

Source:

  • Raconteur, published on November 06th, 2025

What is the AI technology operating model – and why does it matter? A well-designed AI operating model provides the structure, governance and cultural alignment needed to turn pilot projects into enterprise-wide transformation

By Duncan Jefferies

Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?

Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.

“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”

But while the importance of this framework is clear, how should enterprises establish and embed it?

“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.

Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.

“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”

An open approach

The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.

“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”

A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.

“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”

A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”

Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.

“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”

Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.

In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.

 

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OPIT’s Peer Career Mentoring Program
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Oct 24, 2025 6 min read

The Open Institute of Technology (OPIT) is the perfect place for those looking to master the core skills and gain the fundamental knowledge they need to enter the exciting and dynamic environment of the tech industry. While OPIT’s various degrees and courses unlock the doors to numerous careers, students may not know exactly which line of work they wish to enter, or how, exactly, to take the next steps.

That’s why, as well as providing exceptional online education in fields like Responsible AI, Computer Science, and Digital Business, OPIT also offers an array of career-related services, like the Peer Career Mentoring Program. Designed to provide the expert advice and support students need, this program helps students and alumni gain inspiration and insight to map out their future careers.

Introducing the OPIT Peer Career Mentoring Program

As the name implies, OPIT’s Peer Career Mentoring Program is about connecting students and alumni with experienced peers to provide insights, guidance, and mentorship and support their next steps on both a personal and professional level.

It provides a highly supportive and empowering space in which current and former learners can receive career-related advice and guidance, harnessing the rich and varied experiences of the OPIT community to accelerate growth and development.

Meet the Mentors

Plenty of experienced, expert mentors have already signed up to play their part in the Peer Career Mentoring Program at OPIT. They include managers, analysts, researchers, and more, all ready and eager to share the benefits of their experience and their unique perspectives on the tech industry, careers in tech, and the educational experience at OPIT.

Examples include:

  • Marco Lorenzi: Having graduated from the MSc in Applied Data Science and AI program at OPIT, Marco has since progressed to a role as a Prompt Engineer at RWS Group and is passionate about supporting younger learners as they take their first steps into the workforce or seek career evolution.
  • Antonio Amendolagine: Antonio graduated from the OPIT MSc in Applied Data Science and AI and currently works as a Product Marketing and CRM Manager with MER MEC SpA, focusing on international B2B businesses. Like other mentors in the program, he enjoys helping students feel more confident about achieving their future aims.
  • Asya Mantovani: Asya took the MSc in Responsible AI program at OPIT before taking the next steps in her career as a Software Engineer with Accenture, one of the largest IT companies in the world, and a trusted partner of the institute. With a firm belief in knowledge-sharing and mutual support, she’s eager to help students progress and succeed.

The Value of the Peer Mentoring Program

The OPIT Peer Career Mentoring Program is an invaluable source of support, inspiration, motivation, and guidance for the many students and graduates of OPIT who feel the need for a helping hand or guiding light to help them find the way or make the right decisions moving forward. It’s a program built around the sharing of wisdom, skills, and insights, designed to empower all who take part.

Every student is different. Some have very clear, fixed, and firm objectives in mind for their futures. Others may have a slightly more vague outline of where they want to go and what they want to do. Others live more in the moment, focusing purely on the here and now, but not thinking too far ahead. All of these different types of people may need guidance and support from time to time, and peer mentoring provides that.

This program is also just one of many ways in which OPIT bridges the gaps between learners around the world, creating a whole community of students and educators, linked together by their shared passions for technology and development. So, even though you may study remotely at OPIT, you never need to feel alone or isolated from your peers.

Additional Career Services Offered by OPIT

The Peer Career Mentoring Program is just one part of the larger array of career services that students enjoy at the Open Institute of Technology.

  • Career Coaching and Support: Students can schedule one-to-one sessions with the institute’s experts to receive insightful feedback, flexibly customized to their exact needs and situation. They can request resume audits, hone their interview skills, and develop action plans for the future, all with the help of experienced, expert coaches.
  • Resource Hub: Maybe you need help differentiating between various career paths, or seeing where your degree might take you. Or you need a bit of assistance in handling the challenges of the job-hunting process. Either way, the OPIT Resource Hub contains the in-depth guides you need to get ahead and gain practical skills to confidently move forward.
  • Career Events: Regularly, OPIT hosts online career event sessions with industry experts and leaders as guest speakers about the topics that most interest today’s tech students and graduates. You can join workshops to sharpen your skills and become a better prospect in the job market, or just listen to the lessons and insights of the pros.
  • Internship Opportunities: There are few better ways to begin your professional journey than an internship at a top-tier company. OPIT unlocks the doors to numerous internship roles with trusted institute partners, as well as additional professional and project opportunities where you can get hands-on work experience at a high level.

In addition to the above, OPIT also teams up with an array of leading organizations around the world, including some of the biggest names, including AWS, Accenture, and Hype. Through this network of trust, OPIT facilitates students’ steps into the world of work.

Start Your Study Journey Today

As well as the Peer Career Mentoring Program, OPIT provides numerous other exciting advantages for those who enroll, including progressive assessments, round-the-clock support, affordable rates, and a team of international professors from top universities with real-world experience in technology. In short, it’s the perfect place to push forward and get the knowledge you need to succeed.

So, if you’re eager to become a tech leader of tomorrow, learn more about OPIT today.

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