In today’s hyper-competitive world, businesses no longer rely solely on gut decisions or intuition; they depend on data-driven insights to stay agile and make fast, smart decisions. However, data alone isn’t the answer; it’s the enabler to create momentum on a business & technology flywheel: a model where data drives decisions, decisions drive actions, and those actions drive value, propelling the business forward in a self-reinforcing cycle. In a previous post, I used a model to explain how data could cross the borders of applications and domains to bring increasing value at the organizational level.

Context In a recent meetup I organized in my hometown of Lille, I had the pleasure of hosting Jean-Georges Perrin, who provided a comprehensive introduction to data contracts. As a geek, I felt compelled to test this concept to fully grasp its practical implications. The goal of this article is to demonstrate how data contracts can be applied to and add value within a small ecosystem facing cross-domain challenges. To illustrate, I will use my personal experience in the fields I work in, which can be categorized into two separate domains:

In this article, I delve into the concept of RAG, aiming to write a RAG nearly from scratch to view it as a pure engineering problem. Learning by doing from scratch will help me eventually discover a kind of exaptation that can guide my decisions as an engineer and clarify any points of confusion I have in understanding the system. I used information from an article in Go because I am fluent in that language. I will write a step-by-step method to create a simple (though not efficient or effective) RAG, noting discoveries that may be useful for my work as a consultant and engineer.

Using Simon Wardley’s evolution model, I propose a framework for visualizing the maturity of data within a business context, emphasizing the importance of treating data as a product and implementing data contracts to facilitate integration and ensure trust. Ultimately, I suggest that starting with a focus on data-as-a-product is crucial for organizations embarking on their data mesh journey, paving the way for a comprehensive and agile transformation.

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This article explores the transition from a WebSocket-based implementation to a simpler, more direct stream over HTTP in the context of capturing touch screen inputs on Linux.

It begins by introducing the main theme, encapsulated in the statement Everything is a file is a stream of byte. The need to capture finger positions on a touchscreen by reading /dev/input/events in Linux is initially discussed, followed by a dilemma of transferring this data to a JavaScript client in a browser.

Initially, WebSockets are chosen, leading to a discussion on how frameworks often shape our technological choices and the challenges faced in debugging WebSocket connections. The article then introduces an alternative about sending a stream of bytes over HTTP, drawing a parallel to Linux’s approach to handling devices and files.

Serialization, the process of encoding messages for this stream, is discussed next, highlighting the implementation specifics in GoLang and its native advantages. The final section covers how to receive and decode this stream in JavaScript within a worker thread, and then send the decoded messages to the main thread using post requests. The article concludes by reflecting on the benefits of simplicity in technology, urging readers to reevaluate default choices and consider more straightforward solutions to complex problems.

Exploring the innovative concept of Data-as-a-product

This article is about the transformation of data into a strategic asset within organizations. It outlines the pivotal role of data in decision-making, emphasizing the importance of Business Intelligence (BI) in the digital landscape.

The article distinguishes between digital products and data products, highlighting how data-as-a-product enhances data management and supports data products in analytical and operational systems.

Central to this is the application of product thinking to data, aiming to create reliable, accessible, and high-quality data solutions within the framework of data mesh, as conceptualized by Zhamak Dehghani.