Data Modelling Certification Training
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Methodologies used in our Data Modelling Course are designed to help students understand how to choose, use, and interpret the right statistical designs and analyses for real-world data challenges. Understanding the relationships in the data, theory, and application of linear and non-linear regression models, model-building processes, model diagnostics, and corrective procedures are among the topics covered. Enrol today to earn your certification.
Data Modelling Training Course Syllabus
✔ What is data modeling and its significance?
✔ What is Data Warehousing
✔ Conceptual Data modeling
✔ Physical Data modeling
✔ Logical Data modeling
✔ Benefits and scope of data modeling
✔ Basic Conceptual Architecture
✔ Demonstrate fact and dimension
✔ Difference between facts and dimensions
✔ Evolution of RDBMS
✔ RDMS providers
✔ Parts of database
✔ Relational concepts
✔ OLTP vs. OLAP
✔ What is ETL Process
✔ Where ETL Process is used
✔ Scope of ETL in Data warehouse
✔ Topics for Erwin Tool
✔ Introduction to Erwin Tool
✔ Installation and configuration of erwin and Notation
✔ Creation of Entities and attributes
✔ Creation of Relationship
✔ Understanding constraints (PK, FK) in Erwin tool
✔ Creation of Domains, Default Values, Validation rules
✔ Creating Diagrams
✔ Creating Conceptual model
✔ Creating Logical model
✔ Creating Physical model
✔ Creating Subject area model
✔ Generating Scripts from Physical Model
✔ Building reports and Printing in erwin
✔ Forward Engineering
✔ Reverse Engineering
✔ Complete compare of models/schem
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Data Modelling Training FAQ'S
Data modelling is the diagrammatic description of the relationships between the various components. It is the first phase in the database design process. The conceptual model is built initially, followed by the logical model and then the physical model.
Typically, the software development life cycle’s data analysis and design phase is when data models are created.
There are three types of data models – conceptual, logical, and physical. The level of complexity and detail increases from conceptual to logical to a physical data model.
The conceptual model shows a very basic high level of design while the physical data model shows a very detailed view of design.
- The conceptual model will just portray entity names and entity relationships. Figure 1 shown in the later part of this article depicts a conceptual model.
- The Logical Model will be showing up entity names, entity relationships, attributes, primary keys, and foreign keys in each entity. Figure 2 shown in question#4 in this article depicts a logical model.
- The physical Data Model will be showing primary keys, foreign keys, table names, column names, and column data types. This view actually elaborates on how the model will be actually implemented in the database.
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Facts represent quantitative data.
For Example, the net amount due is a fact. A fact table contains numerical data and foreign keys from related dimensional tables. An example of the fact table can be seen from Figure 2 shown above.
OLTP stands for the Online Transaction Processing System & OLAP stands for the Online Analytical Processing System. OLTP maintains the transactional data of the business & is highly normalized generally. On the contrary, OLAP is for analysis and reporting purposes & it is in de-normalized form.
This difference between OLAP and OLTP also gives you the way to choosing the design of schema. If your system is OLTP, you should go with star schema design and if your system is OLAP, you should go with snowflake schema.