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Business Analysis of Data Modeling - Business Intelligence and Top Data Science Course and become a Data Analyst
Master Course of Data modeling is all about 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.
A data model can be thought of as a flowchart that illustrates data entities, their attributes and the relationships between entities. It enables data management and analytics teams to document data requirements for applications and identify errors in development plans before any code is written.
Alternatively, data models can be created through reverse-engineering efforts that extract them from existing systems. That's done to document the structure of relational databases that were built on an ad hoc basis without upfront data modeling and to define schemas for sets of raw data stored in data lakes or NoSQL databases to support specific analytics applications.
Data modeling occurs at three levels—physical, logical, and conceptual.
• A physical model is a schema or framework for how data is physically stored in a database.
• A conceptual model identifies the high-level, user view of data.
• A logical data model sits between the physical and conceptual levels and allows for the logical representation of data to be separate from its physical storage.
This blog post will primarily discuss logical data modeling.
DATA MODELS DESCRIBE BUSINESS ENTITIES AND RELATIONSHIPS
Data models are made up of entities, which are the objects or concepts we want to track data about, and they become the tables in a database. Products, vendors, and customers are all examples of potential entities in a data model.
1. Introduction of database, datamodel and data development process
2. Components of logical data model schema
3. Business data model and data modeling techniques
4. Data modelng tools for SQL server
5. Data modeling in salesforce, power bi and oracle
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