Et après SQL ?

What comes after SQL? This may seem like a bold statement. But is a language created over 30 years ago still relevant for our analytical needs?

SQL was designed for OLTP (Online Transaction Processing), for CRUD operations (Create, Read, Update, Delete).

In the era of data analysis, we now use SQL to transform data, create ad hoc analyses, and develop business intelligence dashboards.

We have created tools (like dbt) to streamline this process and introduce 'software best practices'. We have made SQL our lingua franca for everything related to analysis.

SQL doesn't need to change. It has been working very well for decades. It is the cornerstone of most of our modern databases. It's the data and what we do with it that has changed. Yet, we still rely on fairly basic frameworks (Spark with Hadoop/MapReduce) and have built our analytical semantics on top of SQL to handle data that is no longer rectangular.

Are we missing something? What comes next after SQL?

Drawing from my experiences as a Data Ops engineer, supporting data teams in companies such as Deezer, Olympique de Marseille, Maisons du Monde, etc., this presentation will examine the overlooked flaw introduced by SQL in the world of analysis, how it can be managed, and how new frameworks are paving the way in this field.