5 Challenges of data warehouses

Data warehouses play a crucial role in the data landscape, offering structured and efficient access to data for users with varying technical skills, yet they come with several challenges. These include the need for data to be pre-structured, which can be time-consuming and resource-intensive, and the potential for high storage costs due to the accumulation of historical data. Additionally, data warehouses require pre-planned designs, limiting flexibility for new use cases, and struggle to maintain a single source of truth due to the effort needed to integrate new data sources. They are also not well-suited for unstructured data types like video or audio content, prompting some organizations to explore data lakes or lakehouses as complementary solutions to enhance their data lifecycle. While data warehouses effectively store transaction, operational, and customer relationship data, they may not encompass all organizational data, which can be limiting if new data needs arise, highlighting the importance of evaluating data needs and solutions on a case-by-case basis.