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Modeling Slowly Changing Dimensions

A deep dive into the various SCD types and how they can be implemented in Data Warehouses

Giorgos Myrianthous
TDS Archive
13 min readMay 3, 2024

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Photo by Pawel Czerwinski on Unsplash

In today’s dynamic and competitive landscape, modern organisations heavily invest in their data assets. This investment ensures that teams across the entire organisational spectrum — ranging from leadership, product, engineering, finance, marketing, to human resources — can make informed decisions.

Consequently, data teams have a pivotal role in enabling organisations to rely on data-driven decision-making processes. However, merely having a robust and scalable data platform isn’t enough; it’s essential to extract valuable insights from the stored data. This is where data modeling comes into play.

At its essence, data modeling defines how data is stored, organised, and accessed to facilitate the extraction of insights and analytics. The primary objective of data modeling is to ensure that data needs and requirements are met to support the business and product effectively.

Data teams strive to offer organisations the ability to unlock the full potential of their data but usually encounter a big challenge that relates to how the data is structured such that meaningful analyses can be performed by the relevant teams. This is why modeling dimensions is one of the…

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Giorgos Myrianthous
Giorgos Myrianthous

Written by Giorgos Myrianthous

I strive to build data-intensive systems that are not only functional, but also scalable, cost effective and maintainable over the long term.

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