What Are The Stages Of Data Warehousing?

What are the stages of data warehousing? The four stages are planning, development, operations, and maintenance. This is a very broad overview, but it gives a good sense of how each stage works and what you will need for it. Data warehousing represents a critical part of the information management function for any organization. We will review these four stages in detail, as this article goes on.


The stage in which companies collect, organize, and evaluate information. It helps in reducing the time needed for retrieving data and provides more accurate results. In short, data-warehouse technology helps in achieving maximum efficiency in a business organization.


Planning is an act of establishing the Data Warehousing model. Selecting the data models and defining the data structures. You will select the fact tables or key performance indicators and define their relevant programming interfaces.


The development stage is where you develop your business processes and the data warehouses. You have the initial foundation for your warehousing application with the fact tables and key performance indicators. The next step is to build logical constructs on top of the fact tables and key performance indicators. You can test the application and define its functionality using the data-loading and data-saving functions.


Operations covers the delivery and collection of data structures and the integration of the operational systems. You finalize your data warehouse application and create the integration for the business processes. At this stage, you can convert your data structures into historical data. You can also add functionality to the data warehouses by using the data loading functions and the data-saving functions.

Data warehousing and data integration are interrelated. If one does not complete the other, the whole process of building the data warehouse or data integration will be incomplete. Thus, data warehousing must be completed before the introduction of the data analysis phase.

Data Compilation

This refers to the compilation of the data that has been accumulated over the period of its storage and integration in the data warehouse. In earlier versions of applications, data compilation used to mean the generation of schemas, database templates, etc. With the advancement of the software, the data compilation phase has come to mean the construction of databases, the generation of logical representations and finally the integration of the data into the application. The data sources that are to be integrated should be identified at this stage.

Transaction Processing

This refers to the process of data transformations and application security. There are different types of transaction processing technologies available, including Oltp server transaction processing, IETPs transactions, Java transaction processing etc.

Sharing Data

In case of data warehouses, the source is usually the application server. The final stage is the verification stage, which verifies the data warehouses against the original data.

Data Quality

This is one of the most important aspects of data quality, and involves a number of steps. Like transformations, loading maps and so on. ETL makes sure that the transformation occurs without modifying the original data source. It is one of the most important stages of data modeling as well.

Business Intelligence Goals

Business intelligence (BI) and business intelligence processes (BI/SI) bring managers together with decision-makers at the point of making critical business decisions. BI can be used to gather, manage, analyze and present data that have strategic value. To build a data warehouse system. The ETL process is one of the key stages of data warehousing. ETL ensures that the data warehouse system meets the specific requirements of the decision-makers.


The ETL stage involves extractions of the required data from the data sources. The ETL tools can be in-house or outsourced. Outsource ETL works better for those companies that do not have the resources to maintain their data warehouse systems or to build one on their own. The time taken to complete the project would be much higher when you outsource it.

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