An Enterprise Scenario for Data Warehousing
An enterprise scenario for data warehousing is characterized by the need to integrate data from multiple sources across different departments and business units. In large organizations, data is often siloed, meaning it is stored in separate systems or databases that are not easily accessible to other parts of the organization. This can make it difficult to get a complete picture of the business and make informed decisions. A data warehouse in an enterprise scenario can help solve this problem by providing a central repository for all of the organization’s data.
One of the key requirements of an enterprise data warehouse is scalability. As the amount of data and the number of users accessing the data warehouse grow, the system must be able to handle the increased load. This may require a distributed architecture, where data is spread across multiple servers or even data centers. It may also require specialized hardware and software designed for high-performance data processing.
Another critical aspect of an enterprise data warehouse is security. Data is often one of an organization’s most valuable assets, and protecting it is essential. An enterprise data warehouse must be designed with security in mind, including features such as user authentication and authorization, data encryption, and audit trails to track access to the system.
In an enterprise data warehouse scenario, data quality is also crucial. The data must be accurate, complete, and consistent to ensure that the insights derived from it are reliable. To achieve this, the data warehouse must include data quality checks and validation processes to ensure that data is correctly entered and updated.
Finally, an enterprise data warehouse must be able to support a variety of analytical applications and tools. This includes both business intelligence and data science tools that allow users to visualize and analyze data, extract insights, and make informed decisions. An enterprise data warehouse must be designed to support different types of queries and analytical workloads, such as ad hoc reporting, OLAP analysis, and predictive modeling.
In this chapter, we have explained multiple options like data lake, data lake house and data mesh that can be used to create an enterprise data warehouse, the topic here just describes the need to cater to different aspects when building an enterprise data warehouse.
