A data warehouse is a single schema that organizes a heterogeneous collection of multiple data sources. There are two TECHNIQUES to building a data warehouse. They are as follows:

Top-Down Approach in Data Warehouse:

Following are the major components :

• External Sources - An external source is a location from which data is collected, REGARDLESS of the data format. Structured, semi-structured, and UNSTRUCTURED data are all possibilities.
• Stage Area - Because the data gathered from external sources does not follow a specific format, it must be validated before being loaded into the data warehouse. ETL tool is used for this purpose in the stage area.
• Data-warehouse - After data has been cleansed, it is kept as a central repository in the data warehouse. The meta data is saved here, while the real data is housed in data marts. In this top-down approach, the data warehouse stores the data in its purest form.
• Data Marts - A data mart is a storage component as well. It maintains information about a single organization's function that is managed by a single authority. Depending on the functions, an organization can have as many data marts as it wants. 
• Data Mining - Data mining is the process of ANALYZING large amounts of data in a data warehouse. With the use of a data mining algorithm, it is used to discover hidden patterns in databases and data warehouses.

Bottom Up Approach in Data Warehouse:

Following are the steps involved in the bottom up approach:

• The data is first gathered from external sources (same as happens in top-down approach).
• The data is then imported into data marts rather than data warehouses after passing through the staging area (as stated above). The data marts are built first, and they allow for reporting. It focuses on a specific industry.
• After that, the data marts are incorporated into the data warehouse.