Logo Ust Does Tech
Logo Inverted Logo
  • Posts
  • Databricks
    • Databricks Labs Data Generator
    • Using Auto Loader on Azure Databricks with AWS S3
    • Using and Abusing Auto Loader's Inferred Schema
  • Data Lake
    • Introduction to Data Lakes
    • Deep Dive Into Data Lakes - SQL Bits
  • Data Mesh
    • What is Data Mesh?
    • Data Mesh Deep Dive
    • Data Domain Fictional Case Study in Retail
    • Data Product Fictional Case Study in Retail
  • Data Science
    • Forecasting Methods and Principles
  • SQL
    • Triangulation Arbitrage in SQL
    • CI CD with Synapse Serverless
  • Testing
  • Strategy
    • Why Data Quality is Important
  • Data Modelling
    • Tabular Automation with TMSL and PowerShell - SQL Bits
Hero Image
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).

September 11, 2021 Read
Hero Image
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.

August 17, 2021 Read
Hero Image
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.

August 5, 2021 Read
Hero Image
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.

August 3, 2021 Read
Navigation
  • About
  • Projects
  • Recent Posts
Contact me:
  • [email protected]

Toha Theme Logo Toha
© 2021 Copyright.
Powered by Hugo Logo