2. Built-in deduplication

By 2024, an estimated 90% of global data is expected to be redundant, emphasizing the need for efficient storage management. UltiHash tackles this challenge with its byte-level deduplication algorithm, designed to minimize storage volumes by identifying and eliminating redundant data across all objects, regardless of format. This method can reduce overall storage needs by up to 60%, enabling organizations to scale their data without proportionally increasing capacity requirements.

The deduplication process works by splitting objects into fragments of varying sizes depending on the dataset. If a fragment already exists within the system, it isn’t stored again, eliminating unnecessary duplication across datasets. This ongoing comparison ensures that storage resources are utilized efficiently while maintaining data integrity.

Unlike one-time compression techniques that often add performance overhead, UltiHash’s deduplication runs continuously and is format-agnostic, supporting structured, unstructured, and even compressed data. In certain cases, such as with RAW files, tests have shown volume reductions of up to 74%. This makes UltiHash ideal for environments handling large quantities of redundant data, particularly in AI, machine learning, and media-heavy applications.