Qbeast focuses on Big Data analysis with Data Leverage, providing valuable insights while accessing only the minimum amount of raw data. The key focus of the research team was to merge the scalability and flexibility of Big Data software with the superior efficiency of HPC technology. This union gave birth to a new and innovative architecture for storing, organizing, and analyzing data.

Data Skipping. The usage of an index helps avoid reading the entire dataset, reducing the amount of data transfer involved and speeding up the query.

Approximate Queries. Qbeast enables approximate queries, the ability to provide approximate answers to queries at a fraction of the cost of executing the query.

File Optimization. When writing new data, the file layout could be harmed, producing lots of small files or heavily large ones, making uneasy to retrieve the results with the less noise possible. Optimization fixes the overflowed areas and improves the query useful payload by reading more fine-grained files.