Data Marts pour la BI

Datamarts are specialized subsets of data warehouses designed to meet the analytical needs of a specific department or business function. In the context of Business Intelligence (BI) tools, a datamart goes beyond simply copying tables: data undergoes essential transformations—cleaning, structuring, and summarization—to make it immediately usable for visualization. The focus is on delivering preprocessed, relevant information tailored to specific use cases.

While datamarts offer better performance, ease of use, and ready-to-use data, they also have limitations. They lack flexibility and can lead to functional and technical duplication, which may cause broader governance issues. Additionally, business users remain dependent on technically skilled data analysts to expand their analysis scope, as only they can modify or create new datamarts.

The rise of distributed data warehouses and SQL-based transformation tools has led to diverging approaches between data engineers and data analysts. Data engineers favor centralized, robust solutions aligned with Inmon’s methodology, while data analysts prefer more agile and flexible methods, closer to Kimball’s philosophy.

Even though data is now processed and stored in the same warehouse, it is often organized into distinct zones or schemas, blurring the traditional distinction between datamarts and data warehouses in favor of a more integrated approach.

Theodo’s point of view

We recommend reassessing the use of datamarts, as their adoption has declined over the past decade for the reasons mentioned above. However, this paradigm remains relevant in cases where data engineers and analysts use different tools or when processing capabilities are limited or costly. Instead, we advocate for implementing a semantic layer, which provides greater flexibility and scalability for data analysis.