What type of data model does Qlik Sense implement for in-memory processing?

Qlik Sense implements an associative data model for in-memory processing, which is fundamental to its powerful data interaction capabilities. This model allows users to freely explore data without predefined paths, enabling them to make connections across various datasets. The associative model contrasts with traditional relational models, where data is often structured in a more linear and rigid manner, requiring users to follow specific joins or paths to access related data.

In Qlik Sense, the associative model facilitates seamless data discovery, as any data point can be related to others in a flexible way. Users can see how selections impact the entire dataset, with associations highlighted in real-time. This method leverages in-memory processing to provide fast and responsive data analysis, allowing users to engage with large volumes of data efficiently.

Other models such as the relational data model or the hierarchical data model impose stricter structures that limit flexibility and exploration. Relational models require complex joins to navigate relationships, while hierarchical models organize data in parent-child structures, which can restrict the ways users can analyze and interact with data. The NoSQL data model, while designed for unstructured data and scaling, does not provide the same associative capabilities and interaction advantages that Qlik Sense's model offers.

Thus, the associative data model is the optimal choice for Q