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

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

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

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

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.

Learning Objectives – Implementing a data warehouse Project.

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

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

After the completion of Data warehousing course at Edureka, you will be able to:

1. Understand the concept and need of Data warehouse

2. Implement concepts of dimension and fact table

3. Implement data modelling, normalization and schema concepts 4. Model a Data warehouse

5. Implement ETL jobs

This course is a foundation to anyone who aspires to become a Data warehouse Architect, a Data warehouse Developer or a Data warehouse Business Analyst in the field of Data warehousing and Business Intelligence. The following professionals can go for this course :

1. BI /ETL Professionals

2. Project Managers

3. Testing Professionals

4. Mainframe Professionals

5. Analytics Professionals

6. Software Developers and Architects

The pre-requisites for this course includes basic understanding of Databases.

As we move from intuition based decision making to factual decision making, it is increasingly important to capture data and store it in a way that allows us to make smarter decisions. This is where Data warehouse comes into picture. There is a huge demand for Data warehousing professionals and this course acts as a foundation which opens the door to a variety of opportunities in Business Intelligence space.