What type of data model is primarily used in SAP HANA for complex data analytics?

The choice of a multidimensional data model as the primary model used in SAP HANA for complex data analytics is rooted in its ability to efficiently handle analytical queries and support multidimensional data structures. This model allows organizations to analyze data across multiple dimensions—such as time, geography, and products—simultaneously.

By structuring data into dimensions and measures, the multidimensional model optimizes query performance and simplifies the data exploration process for end-users. It enables the effective use of features like slicing, dicing, and pivoting which are vital for analytical processes. Furthermore, SAP HANA leverages in-memory computing, which enhances the performance of analysis on multidimensional data models.

While relational data models are essential for transactional systems, they may not provide the same level of analytical efficiency as the multidimensional approach within SAP HANA. Similarly, the object-oriented data model primarily focuses on the representation of data, often necessary for specific applications, and doesn't inherently cater to the complexities of data analytics. The graph data model is excellent for representing relationships between data points but tends to be less utilized for broad, multidimensional analytics compared to the multidimensional model. Thus, the multidimensional data model stands out as the most suitable and effective choice for complex data analytics in