SQL (Structured Query Language) is a programming language used by many people in the data business. It stands for Structured Query Language. According to a 2023 survey by StackOverflow.com, 40–50% of developers globally use SQL. Millions of data analysts, data engineers, and database administrators also use this language. It is becoming increasingly difficult to avoid using SQL in an IT environment.

Today, I am therefore going to explain how SQL has become so powerful across all sectors and industries, particularly in the data business. Once you have read this article, SQL will no longer hold any secrets for you!

SQL: The English of the Data World

Back then, the internet did not exist, and relational databases were in their infancy. The concept of storing data in rows and columns, like a spreadsheet, was revolutionary.

It was proposed by Edgar F. Codd, a British computer scientist working at IBM, in a paper titled "A Relational Model of Data for Large Shared Data Banks." This paper laid the foundation for the relational database model, which treats data as relations (tables) that can be queried using logical structures.

To make this idea practical, IBM started developing a prototype system called System R, and along with it, a new language was born: SEQUEL, which later became SQL.

SQL wasn’t just another programming language; it was straightforward to use and understand. Rather than telling the computer how to find the information, you simply state what you want, and the system determines the most efficient method. This made it much simpler than older programming languages, which required very specific, step-by-step instructions.

 In the 1980s, the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO) adopted SQL as the standard language for relational databases. Consequently, major database systems such as Oracle, IBM DB2, Microsoft SQL Server, MySQL and, more recently, PostgreSQL were all built around this standard.

The power of SQL lies in its simplicity and flexibility. Today, even non-technical professionals use SQL through no-code or low-code interfaces.

In the age of cloud computing, big data and real-time analytics, not only has SQL survived, it has thrived. Technologies such as Google BigQuery, AWS Redshift and Databricks SQL demonstrate that SQL remains at the core of the modern data stack. Data professionals love it for its powerful syntax, widespread adoption, and compatibility with virtually every database.

In fact, we could say that SQL is the official language of data worldwide, just as English is the universal language of communication.

As the internet grew and tech companies began processing large volumes of data, the limitations of traditional SQL databases became apparent.

While traditional SQL databases were ideal for structured data such as customer names, order amounts and dates, they were not well suited to social media content, chat apps and the massive, fast-changing data from mobile apps and IoT devices.

The Rise of NoSQL: Flexible Data for a Fast-Moving World

A new type of database emerged in the 2000s to solve this: NoSQL (short for 'Not Only SQL'). Rather than being a single technology, NoSQL is a group of database systems that are more flexible and better suited to certain types of data and high-speed applications. Unlike SQL, which uses fixed table structures, NoSQL databases can store data in various forms, such as documents, key-value pairs or graphs.

In the context of generative AI, NoSQL's features benefit the models since they often require large and varied datasets, such as user inputs, logs, text and images. Databases such as MongoDB allow documents or records to be stored without a strict structure, unlike SQL tables.

Moreover, NoSQL databases are typically designed with horizontal scalability in mind. This means they can easily be expanded across multiple servers — a necessity for the high throughput and low latency demanded by modern applications such as recommendation engines, real-time analytics and AI-powered services.

As the volume of data continues to grow exponentially and AI systems become more integral to software platforms, NoSQL's role in managing and supporting these technologies becomes increasingly vital.

Although NoSQL is not intended to replace relational databases entirely, it offers a complementary approach. Indeed, many modern systems adopt a polyglot persistence strategy, using SQL databases for structured transactions and NoSQL databases for unstructured or rapidly changing data.

In this evolving data landscape, NoSQL has secured its place as a cornerstone technology for scalable, flexible and AI-ready applications.

SQL and NoSQL: The Twin Pillars of Modern Data Infrastructure

Generative AI applications often deal with massive amounts of data and require fast performance, especially when serving millions of users in real time. NoSQL databases are designed to scale easily, enabling systems such as Cassandra and DynamoDB to handle large volumes of data across multiple servers without slowing down.

Almost every organisation stores its data in relational databases, whether that's customer information, sales records or website activity. SQL is the universal language for querying this data. It is both simple and powerful. Unlike downloading files or copying spreadsheets, SQL provides analysts with clean, secure and controlled access to live data. 

You can trust the results because you’re pulling data directly from the source. SQL has become essential for data analysis because it is the fastest and most reliable way to access, manipulate and understand data where it is stored — in databases. Knowing SQL is like knowing how to read and write in the language of data.