Concepts for Data Engineers: Dimensional Data Modelling
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In this series I’m introducing several important concepts that new Data Engineers should be aware of. The other topics I talked so far:
I’m introducing several important concepts that new Data Engineers should be aware of. The other topics I talked so far:
✅ Data Modelling
✅ CDC
✅ Idempotency
✅ ETL x ELT x EL
✅ Kappa x Lamda Data Architectures
✅ Slowly Changing Dimensions — SCD
✅ 10 Concepts all Data Engineers should know
I also have 2 series about python:
🐍 Efficient Python
🐍 Software Engineering with Python
Model you data to look good, but not like Gisele Bundchen…
Introduction
Data modeling plays a crucial role in the design and development of databases. It is the process of creating a conceptual representation of the data and its relationships within an organization. Effective data modeling ensures that the database accurately represents the real-world entities and their associations, providing a solid foundation for data storage, retrieval, and analysis. In this article, we will explore the concept of data modeling for databases, its importance, and its relationship with Kimball’s dimensional modeling approach. This text has a focus on exploring the most common dimensional models: Star and Snowflake.
Importance of Data Modeling
Data modeling is essential for several reasons:
- Data organization: Data modeling helps in organizing and structuring data in a logical manner. It defines the entities, attributes, and relationships, ensuring that data is stored in a consistent and coherent manner.
- Data integrity: By defining relationships and constraints, data modeling ensures the integrity of the data. It helps enforce business rules and prevent inconsistencies or inaccuracies in the database.
- Data integration: Data modeling enables the integration of data from various sources into a unified database. It provides a common structure and vocabulary for…
