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@article{187154,
        author = {Suvarna Lahanu Ghogare},
        title = {BLOCK LEVEL DATA DEDUPLICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4599-4606},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187154},
        abstract = {In today’s world of massive data generation, efficient storage utilization has become a critical challenge. Data deduplication is a storage optimization technique that eliminates redundant data by retaining only one unique copy of repeating blocks. This paper presents the implementation of block-level data deduplication using Python, which divides files into smaller fixed-size or variable-size blocks, computes their hash values, and stores only unique blocks. The system uses hashing algorithms such as SHA-256 to identify duplicate blocks and a simple indexing mechanism to manage block references. The Proposed paper check identical data blocks are detected across files, storage space is reduced significantly, The Python-based implementation highlights the feasibility of deduplication in research and teaching environments, providing a foundation for extending this work to cloud storage systems, backup solutions, and file versioning platforms.},
        keywords = {Data Deduplication, Python, Storage Optimization, Hashing, Cloud Storage, Block-Level Deduplication},
        month = {November},
        }