Modelling for Data Warehouse
Data modelling for a data warehouse is the process of designing the structure and relationships of data entities within a data warehouse environment. It involves creating a conceptual, logical, and physical representation of the data to support efficient data storage, retrieval, and analysis.
The data modelling process begins with a thorough understanding of the business requirements and objectives of the data warehouse. This includes identifying the key data entities, their relationships, and the desired analytical outcomes.
Data modelling process going through Conceptual, Logical and Physical phase and understanding business requirements and how to identify dimensions and facts, as One of the key steps in data modelling for a data warehouse where dimensions represent the descriptive attributes used for analysis, while facts are the numeric measures or metrics that provide the basis for analysis.
By the end of the workshop, participants will have a solid understanding of data modelling concepts and different techniques such as Dimensional modelling or Data Vault, and will have created a data model based on a sample business case.
- Data architects
- Data mart developers
- Business analysts
- Basic understanding of databases and SQL
- Overview of data modelling for data warehouse
- Introduction to Entity-Relationship (ER) modelling and dimensional modelling
- Key concepts and terminology
- Business requirements and objectives
- Techniques for capturing and documenting business requirements
- Creating high-level data entities and relationships
- Techniques for creating a logical data model
- Normalization and denormalization techniques
- Mapping business requirements to data entities and relationships
- Creating a logical data model based on the conceptual data model
- Introduction to dimensional data modelling
- Star schema and snowflake schema design techniques
- Identifying dimensions and facts
- Creating a dimensional data model based on the logical data model
- Introduction to physical data modelling
- Techniques for creating a physical data model
- Mapping the dimensional data model to a specific database technology
- Indexing, partitioning, and other physical implementation details
- Iterative refinement of the data model based on performance considerations and evolving business requirements
- Best practices for data modelling for data warehouse
For all inquiries regarding education, please contact us at firstname.lastname@example.org.