What’s Next After SQL?

It sounds like a hot take. But does a language created more than 30 years ago is still relevant to our analytics need?

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

In the advent of data analytics, we now use SQL to transform data. To create ad-hoc analysis. To create business intelligence dashboards.

We have created tools (e.g. dbt) to streamline such process. To bring “software best practices”. We have made SQL our de facto lingua for anything analytics related.

SQL doesn’t need to change. It’s working fine for decades. It’s the keystone of most of our modern world databases.

It’s the data and what we do with it that has changed. Still, we only rely on quite low-level frameworks (Spark with Hadoop/MapReduce) and we have built our analytics semantic on top of SQL to deal with data which is not rectangular anymore.

Are we missing something ? What’s next after SQL?

Coming back on my experiences as a data ops engineer, supporting data team at company like Deezer, Olympique de Marseille, Maisons du Monde, etc. this talk will look at the overlooked flaw introduced by SQL in the analytics world, how it can be managed and how new frameworks are leading the way in that problem space.