First what Power BI?
Power BI is a tool created by Microsoft to turn raw data into interactive insights. IN other words, it's also described as a visualization tool.
Those who already know how to use Microsoft Excel may find it faster to pick up Power BI, as the interface has a lot in common with Excel.
It's like the advanced excel,as its capable of handling huge amounts of data.
What's Data Modelling?
Data modeling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows. Data models provide a blueprint for designing a new database or reengineering a legacy application. Overall, data modeling helps an organization use its data effectively to meet business needs for information.
Data models define how data is structured, related, and ultimately implemented. They serve as blueprints for organizing information, ensuring consistency, and aligning database structures with business needs.
Types of Data models;
- Conceptual Data Models-these data model defines what system contains. The purpose is to organize, scope and define business concepts and rules. 2.Logical Data Model; Defines HOW the system should be implemented regardless of the DBMS.The purpose is to developed technical map of rules and data structures. 3.Physical Data Model;This Data Model describes HOW the system will be implemented using a specific DBMS system.
What is a Database Schema?
A database schema is a formal description of how data is structured or organized within a database.
Components of Database Schema
- Physical Database Schema
- Logical Database Schema >> as defined before ...
Types Of Database Schemas:
Here we going to look at two main schemas;
- star schema-is a data modelling structure used to facts table & is surrounded by dimensional tables. Is commonly used in data warehousing
2.Snowflake schema; is an extension of star schema where dimensional tables are subdivided to more sub-dimensional tables.
It supports complex queries.
KEY NOTE';
Importance of Data Modeling: Essential for understanding business requirements, ensuring data consistency, improving system performance, and reducing errors.
Fact and Dimension Tables: Core components of data modeling, with Fact tables holding quantitative data and Dimension tables providing contextual details.
Star Schema: A simple, denormalized structure that offers fast query performance, ideal for straightforward analytics tasks.
Snowflake Schema: A complex, normalized structure that reduces data redundancy and saves storage space, suited for detailed analysis.
Normalization vs. Denormalization: Techniques to balance between storage efficiency and query performance.
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