In this article, I share my critical view on the current state of data engineering, dominated by heavyweight platforms like Spark and Databricks, and introduce Sail, an open-source engine built on top of Apache Arrow and DataFusion, written in Rust, that offers a new path: lightweight, efficient, and powerful.
The conclusion warns that the real risk isn’t using AI, but delegating to it without critical thinking. In a chain where everyone pretends to know thanks to AI, decisions are built on illusions. The result: projects that appear viable, endorsed by experts who no longer think. A black swan foretold by collective complacency.
This essay explores how the everyday use of AI tools like ChatGPT and Copilot can transform the way we work, learn, and think, creating a false sense of knowledge. A critical reflection on dependence, boundaries, and the need to preserve judgment in an increasingly automated world.
We explore in detail what typeclasses are, how to define them in Scala 2, and how they are implemented in other languages such as Haskell and Rust. Through practical examples, we demonstrate how to model behaviors clearly and scalably using this powerful design pattern in functional programming. We also discuss automatic derivation and the improvements Scala 3 brings to this area.
We analyzed different functional pattern designs to address the abstraction of a functionality and its implementation.
My first impressions of Rust from a functional programming background (Scala and Haskell). A mix of excitement, frustration, and a paradigm shift in thinking.
Defining the serializer and transformation from Yaml to Core
Defining the model core from Scala to Spark