Enabling Self-Service OLAP on NoSQL Data Lakes

Online analytical processing plays a crucial role in data analysis by providing a powerful framework for efficiently exploring and understanding multidimensional data. With the increasing complexity and volume of data in modern organisations, the main challenge is to build OLAP cubes from distributed heterogeneous sources while considering attribute and relationship overlapping across the sources and to let analysts create their cubes on-demand to maximise their usefulness. We propose a self-service, metadata-driven approach for designing and generating OLAP cubes from heterogeneous NoSQL sources composing a data lake. We illustrate our approach through a realistic scenario where the data is scattered and overlaps across different sources. We present a proof-of-concept application that implements our proposed approach. An assessment of our proposal is conducted to highlight the efficacy and usefulness of the generated cubes for analytical purposes.