Object storage is facing a dilemma due to its massive growth, with only less than 1% of the generated data being stored and even less for business value. The format of object storage makes it difficult to analyze and draw meaningful conclusions from the data. Unlike traditional databases, object storage is not designed for diverse, disjoined, and disparate data sources at scale, making it challenging to prepare for traditional analytics. Manual transformation and loading of data into analytics tools are required, which can be expensive in terms of time, money, and resources. The big data frameworks used to prepare object storage for analytics also have complexities and drawbacks that make them difficult to use.