A Data Warehouse, is not just a rich repository of company data. It is also an overall strategy and process for making a cutting edge decision support system. One of the main objectives of a Data Warehouse it to bring together various information from several sources whose platforms could be totally different from one another but the Data Warehouse has the responsibility of putting all these disparate data format into a unified data system that can be used for making Business decisions.

In data warehousing, there is common term called ETL which stands for Extract, Transform and Load.

Data Loading is dependent on the specifications of the database management system that is powering the Data Warehouse.

In general, before data can be loaded, the database and the tables to load must have been created already. There are many utility programs available which can build databases and define the user table with the SQL CREATE TABLE statements. When the load process begins, the database system typically builds primary key indexes for each of the tables which have a primary key. Also, user-defined indexes are also built.

In some really large databases especially those used in data warehouses, it is common to encounter several stages when data loading. It is also common in Data Warehouse implementations to have data loaded into the database from an input file.

Data Warehouse typically employs automated data loading. During the input stage of this loading process, the database validates syntaxes and control statements. It then inputs records, monitor progress and status which is indicated by the error handling and cleanup functions.

At the conversion stage, input records are transformed into row format. Data is then validated and checked for any referential integrity. All arithmetic and conditional expressions are defined within each input column specification. Finally, data is written in the rows of the table.

Data Loading could be a critical process when the design and implementation of a Data Warehouse is not done well or performed in a controlled environment. Contingency measures must be prepared during the data loading process in case of an administration failure.

When such failure occurs, the administrator should be ready with the knowledge of the structure of the processes and the whole database in particular and the Data Warehouse in general. All specific traces of the processes being executed should be tracked in down not just.

In fact, due to the complexity of the Data Warehouse loading process, there are a lot of specialized Extraction, Transformation, Loading (ETL) software applications which can be bought in the market today.

The most important benefits that can be derived from these tools include easy identification of relevant information inside the data source; easy extraction or retrieval of the information; simple customization and integration of different kinds of data coming from a wide array or disparate data sources into a unified common format; fast cleaning of resulting data set based on define Business Rules; and efficient propagation of data to the Data Warehouse or Data Marts.

Data Loading is part of a larger and more complex component of the Data Warehouse architecture called Data Staging. Complex programming is often involved in data staging. This component also often involves analysis of quality data and filters which can identify certain patterns and data structures within the existing operational data.

But whether a database administrator uses data loading tools or generates his own programming codes, one of the most effective ways to manage a Data Warehouse is to develop a good Data Warehouse data loading strategy.