A data warehouse is a centralized system that integrates and stores data from various enterprise applications, facilitating analysis and decision-making. It is designed to provide consistent, historical data for performance analysis and trend prediction, while maintaining key properties such as subject orientation, integration, time-variance, and non-volatility. The ETL process, which includes extraction, transformation, and loading of data, ensures data quality and consistency before it is stored in the data warehouse.

1. DATA WAREHOUSE

2. What is a Data Warehouse
 A Data Warehouse can be defined as a centralized, consistent data
store or Decision Support System (OLAP), for the end business users
for analysis, prediction and decision making in their business
operations.
 Data from various enterprise-wide application/transactional source
systems (OLTP), are extracted, cleansed, integrated, transformed
and loaded in the Data Warehouse.

3. Benefits of data warehouse
 A data warehouse helps to integrate data and store them
historically so that we can analyze different aspects of business
including, performance analysis, trend, prediction etc. over a given
time frame and use the result of our analysis to improve the
efficiency of business processes.
 Data warehouses are built to facilitate reporting on different key
business processes of an organization, known as KPI.
 Data warehouse can be further used for trend prediction, forecasts,
pattern recognition etc.

4. Properties of a Data Warehouse
 Subject Oriented - Data Warehouse is designed based on the major subjects areas of
the business, rather than the major application areas of the enterprise. The ultimate
necessity is to store decision-support data rather than application-oriented data.
 Integrated - The data in Data Warehouse comes from different enterprise-wide
application source systems. The source data is often inconsistent in nature. During the
Integration process, source data must be made consistent in Data Warehouse so that
the data becomes homogeneous and uniform.
 Time-Variant - The source data in the Data Warehouse is only accurate and valid at
some point in time or over some time interval.
 Non-Volatile - Data in Data Warehouse is not updated or replaced in real time but is
refreshed from operational systems on a regular basis. New data is always added
incrementally integrating it with the previous data set.

5. Schematic View of a data warehouse

6. ETL process
ETL (Extract, Transform and Load) is a process in data warehousing
responsible for pulling data out of the source systems and placing it
into a data warehouse. ETL involves the following tasks:
 extracting the data from source systems (SAP, ERP, other operational
systems), data from different source systems is converted into one
consolidated data warehouse format which is ready for
transformation processing.
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7. Transforming the data may involve the following tasks:
 Applying business rules
 Cleaning (e.g., mapping NULL to 0 or "Male" to "M" and "Female" to
"F" etc.),
 Filtering (e.g., selecting only certain columns to load)
 Splitting a column into multiple columns and vice versa
 Joining together data from multiple sources
 Transposing rows and columns
 Applying any kind of simple or complex data validation
loading the data into a data warehouse or data repository other
reporting applications

8. ETL layer is mostly responsible for 2 types of treatments on the
data:
 Data Integration - process of combining heterogeneous data sources in to a
single queriable schema so as to get an unified view of these data.
 Qualitative Treatment - so that the validity and quality of the data can be
checked (and if required corrected) before loading to the data warehouse.

9. THANK YOU