DWH concepts

What is Data Warehouse ?
What is OLAP and OLTP ? Differentiate both.
Explain data warehouse architecture ?
What is  data mart ? What is the difference between data warehouse and data mart ?
What is ER Modelling ?
What is data model ? Explain the types ?
What is dimensional modelling ?
What are dimensions ? What are the types of dimensions ? Explain
What are facts ? What are the types of facts ? Explain
What is start schema ?
What is snow flake schema ?
What is granularity ? Explain.
What is CDC ? Explain Full load and incremental load ?

What is Data Warehouse ?
A data warehouse is a copy of transaction data specifically structured for query and analysis.
It usually contains historical data derived from transnational data.
It separates analysis workload from transaction workload and enables an organisation to consolidate data from several sources.

 Characteristics of Data Warehouse
Subject Oriented

Subject Oriented 
Examples : 
1) Banking transactions where transaction amount is the subject
2) Health care(Medicare) transactions where claims is the subject
3) Education where student transactions like marks are the subject.

A data warehouse integrates data from multiple data sources.
i.e., Data can be gathered from different sources and integrate/merge into single stack.
For instance : In Banking domain a transaction can be identified by two sources. Say, transaction type and transaction channel. Transactions can be integrated with these two sources.

Time variant : 
Historical data is stored in data warehouse.
For instance : 
In Banking domain using DWH one can retrieve 1 month old data, 2 month old data or 6 months or 2 years or even older data.
In Banking domain using OLTP one can retrieve often the most recent data.
Example : In OLTP , a transaction can hold most recent location of a customer whereas in DWH all old addresses of customer can be stored.

Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.