Uma Maheswara Rao Gangumalla

In the domain of data-intensive GenAI (Genetic Artificial Intelligence) applications focused on retrievable augmented generation, efficient management and analysis of large and evolving datasets are critical. Traditional approaches often involve reloading entire datasets for analysis, which can be inefficient and resource-intensive.

In the era of explosive data growth, scalability is paramount for any storage solution. This abstract focuses on the scalability aspects of Apache Ozone, a distributed object storage system designed to handle the ever-increasing demands of modern data-intensive applications.

In today’s data-driven world, organizations are faced with unprecedented volumes of data and increasingly complex storage requirements. To address these challenges, Apache Ozone emerges as a game-changing solution, redefining the landscape of distributed object storage systems.

Data integrity is a critical requirement in storage systems, and many of them support erasure coding. Checksum calculations are relied upon by storage systems to ensure integrity, and the computed checksum data is stored as well. However, in erasure coding, checksum bits need to be regenerated when