1974 May. Donald Chamberlain and Raymond Boyce released a paper — SEQUEL which was a structured query language. It could be used to manage and sort data. Since SEQUEL has been copyrighted by another company, it was renamed Structured Query Language (SQL). Database companies such as Oracle adopted it together with relational database products in the 1970s. The rest is history.

SQL is now 50 years old. It was designed and adopted around databases. It could manage data. We could interact with data. It ranks third among the most popular languages used by programmers. It facilitates the placement of programmers. Some other equally old languages are COBOL (1959) and FORTRAN (1958). They have become legacy languages. SQL is still being used even for AI and analytics.

Why has it survived so long? It is not easy language. It has a peculiar syntax. Database vendors must support SQL. Each vendor has his quirks and nuances to implement this support. The approach for one database may change from that of another database. In SQL, there could be mistakes. The consequences are disastrous.

SQL is based on strong mathematical theory. It is effective and support the use cases it is designed for. SQL combined with relational databases is mapping the data. It is reliable. It is scalable. SQL works.

It returns multiple rows per single request. It is easier to get data on what is happening within a dataset, and within the business and its apps.

SQL makes it easier to compartmentalize and segregate information into a number of tables. Tabulation makes it easy to use the data for different tasks.

SQL remains contemporary by moving with the times. It has added support for geographic information system (GIS) data. It can be combined with vector data. Vector searches could be conducted for generative AI.

There were attempts to replace SQL. NoSQL data bases were developed to replace relational databases. Instead of replacement, such databases added their own SQL-type languages replicating some features of SQL.

NLP advocates called for doing away with SQL’s standardized and clunky approach. Still such attempts led to methods that were as much clunky as what they tried to replace. Generative AI may take on the task of writing SQL for developers. LLMs have already been exposed to large quantities of SQL code while being trained.

SQL may move behind the curtain, but will continue to pay a crucial role in how we interact with the data and use data. SQL is here to stay.