Data Product Fictional Case Study: Retail
Background In a previous post, we explored what the data domains could look like for our fictional retailer - XclusiV. In this post, we will explore how the data products could work in this fictional case study, including how pure data consumers would handle the data - particularly those consumers who have a holistic view of an organisation (also a group of consumers for whom a traditional analytical model is perfect).
Data Domain Fictional Case Study: Retail
In previous posts we’ve understood what is Data Mesh and gone into greater detail with regards to the principles. In this next series of posts I want to use a fictional case study to explore how the underlying principles could work in practice. This post will introduce the fictitious company; the challenges it faces; and how the principle of decentralised data ownership and architecture, with domain alignment, would work. Fictitious Company: XclusiV XclusiV is a luxury retailer operating in multiple countries.
Data Mesh Deep Dive
In a previous post, we laid down the foundational principles of a Data Mesh, and touched on some of the problems we have with the current analytical architectures. In this post, I will go deeper into the underlying principles of Data Mesh, particularly why we need an architecture paradigm like Data Mesh. Let’s start with why we need a paradigm like Data Mesh. Why do we need Data Mesh? In my previous post, I made the bold claim that analytical architectures hadn’t fundamentally progressed since the inception of the Data Warehouse in the 1990s.
What is Data Mesh?
To be able to properly describe what Data Mesh is, we need to contextualise in which analytical generation we currently are, mostly so that we can describe what it is not. Analytical Generations The first generation of analytics is the humble Data Warehouse and has existed since the 1990s and, while being mature and well known, is not always implemented correctly and, even the purest of implementation, comes under the strain of creaking and complex ETLs as it has struggled to scale with the increased volume of data and demand from consumers.