Concepts for Data Engineers: Data Modelling Topics
There is much more than star or snowflake schema when we talk about data modelling
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Followed by the immense amount of reader on my post about dimensional data modelling, I realised that more people than I expected undervaluate this topic. And so, I decided to write a list of other important topics and concepts within data modelling that all data professionals should be aware of, and take advantage of this knowledge.
You might be interest in this series where 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
Entity-Relationship (ER) Modelling
In ER modeling, you learn to identify entities, their attributes, and relationships between entities. ER diagrams serve as powerful visual tools to depict the logical structure of data. You c
996an check more details about it here in this page. Check this also.
Relational Data Model
The relational data model is a widely used approach that organizes data into tables consisting of rows and columns. Understanding this model involves grasping primary keys, foreign keys, and how to establish relationships between tables. To delve deeper into the relational data model and its principles, you can visit this page.
Normalization
The process of normalization allows you to eliminate data redundancy and enhance data integrity. By studying normal forms like first normal form (1NF), second normal form (2NF), and third normal form (3NF), you gain insights into structuring data efficiently. Go deeper in your knowlege in this page.
