Data Warehousing - Self-Paced


$ 170 - (Rs 10,952)
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Important information

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  • Online

This Data warehousing course enables participants with concepts of Data warehousing like Facts, Schema, Metadata, Normalization, Data transformation, Dimensional Modeling and ETL Concepts.

Important information

What you'll learn on the course

Data warehousing

Course programme

1. Introduction to Data Warehouse

Learning Objectives - Discussing about the basic concepts of a data warehouse and why it is needed. Difference between an operational system and an analytical system, Datamarts. Approaches to build a data warehouse.

Topics -
1. What is a data warehouse? - Definition and explanation of the four terms - subject oriented, integrated, non volatile and time variant

2. Need for a data warehouse

3. Difference between a database and a data warehouse. OLTP and OLAP?

4. Datamart - The smaller cousin of the data warehouse

5. ODS - Operational Data Store - Definition and explanation of 4 terms - Subject oriented, Integrated, Current, Volatile, Detailed

6. Benefits of ODS

7. Design approach - Top down approach, bottom up approach, Federated

2. Dimension and Fact

Learning Objectives - Learning what a dimension and a fact is, the different types of dimensions and facts. Reporting concept of Hierarchy.

Topics -
1. Dimensions and facts - What are dimensions and facts?

2. Types of dimensions - emphasis on SCD 1,2,3 implementations

3. Types of facts

4. What are hierarchies - Types of Hierarchies

3. Normalization and Schemas

Learning Objectives - Organizing data in multiple tables. Understanding normalization and its different forms. Learning what is a schema and the different types of schemas along with meta data.

Topics -
1. Normalization

2. Schemas - What is a schema. Types - Star, Snowflake, Galaxy

3. Significant role of meta data

4. Modeling

Learning Objectives - Understanding principles of requirement gathering to build a warehouse and dimensional modeling.

Topics -
1. Requirement gathering

2. Principles of dimensional modelling

3. Modeling - ER diagrams

5. ETL in Detail

Learning Objectives - Understanding where will the data come from and how will the data come and Populating the warehouse.Learning concepts of Extracting data, Transforming data and Loading the data into different tables.

Topics -
1. ETL Concept - Architectural components - like Source, Staging, Atomic, Dimension

2. Transformation - Data Validation, Data Accuracy, Data Type Conversion, Business Rule Application

3. Data Loading techniques.

6. Project

Learning Objectives - Implementing a data warehouse Project.

Topics - Discuss a project, its problem statement, probable solutions, and implement one solution.

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