A data warehouse is a subject oriented, integrated, time variant, non volatile collection of data in support of management's decision making process. To Learn More Follow Below Link: http://bit.ly/143T8Ym

1. Tekslate
Data Warehouse Free Tutorial

2. Introduction to Data
Warehouse
What is a Data Warehouse?
According to Inmon, famous author for several data warehouse books,
"A data warehouse is a subject oriented, integrated, time variant, non
volatile collection of data in support of management's decision making
process".
Example
In order to store data, over the years, many application designers in
each branch have made their individual decisions as to how an
application and database should be built. So source systems will be
different in naming conventions, variable measurements, encoding
structures, and physical attributes of data. Consider a bank that has got
several branches in several countries, has millions of customers and the
lines of business of the enterprise are savings, and loans. The following
example explains how the data is integrated from source systems to
target systems.

3. Data Warehouse Architecture

4. Data Warehouse & Data Mart
A data warehouse is a relational/multidimensional database that is
designed for query and analysis rather than transaction processing. A data
warehouse usually contains historical data that is derived from transaction
data. It separates analysis workload from transaction workload and
enables a business to consolidate data from several sources.
In addition to a relational/multidimensional database, a data warehouse
environment often consists of an ETL solution, an OLAP engine, client
analysis tools, and other applications that manage the process of
gathering data and delivering it to business users.

5. Types of Data Warehouses
There are three types of data warehouses
Enterprise Data Warehouse
ODS(Operational Data Store)
Data Mart
Enterprise Data Warehouse - An enterprise data warehouse provides a
central database for decision support throughout the enterprise.
ODS(Operational Data Store) - This has a broad enterprise wide scope,
but unlike the real entertprise data warehouse, data is refreshed in near
real time and used for routine business activity.
Data Mart - Datamart is a subset of data warehouse and it supports a
particular region, business unit or business function.

6. Types of Schema's in Data
Warehouse
There are four types of schemas are available in data warehouse.
Star Schema
Snow Flake Schema
Galaxy Schema
Fact Constellation Schema
Out of which the star schema is mostly used in the data warehouse
designs. The second mostly used data warehouse schema is snow flake
schema
When we consider an example of an organization selling products
throughtout the world, the main four major dimensions are product,
location, time and organization. Dimension tables have been explained
in detail under the section Dimensions. With this example, we will try to
provide detailed explanation about STAR SCHEMA.

7. Popular Database Vendors
Database - RDBMS
There are a number of relational databases to store data. A relational
database contains normalized data stored in tables. Tables contain
records and columns. RDBMS makes it easy to work with individual
records. Each row contains a unique instance of data for the categories
defined by the columns.
RDBMS are used in OLTP applications(e.g. ATM cards) very frequently
and sometimes datawarehouse may also use relational databases.
Please refer to Relational data modeling for details to know how data
from a source system is normalized and stored in RDBMS databases.

8. ETL Concepts in Data
Warehouse
ETL Concepts
Extraction, transformation, and loading. ETL refers to the methods
involved in accessing and manipulating source data and loading it into
target database.
The first step in ETL process is mapping the data between source
systems and target database(data warehouse or data mart). The
second step is cleansing of source data in staging area. The third step
is transforming cleansed source data and then loading into the target
system.
Note that ETT (extraction, transformation, transportation) and ETM
(extraction, transformation, move) are sometimes used instead of ETL

9. Issues
Interview Questions
How to’s
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