Abstract:
Data duplication technology uses file checksums to quickly and accurately identify redundant data. A checksum can identify redundant data. However, false positives occur. We must compare new data with stored data to avoid false positives.
File data checksum extraction speeds up false positive elimination in current research. However, the target file stores user id, filename, size, extension, checksum, and date-time table. The system calculates the checksum and compares it to the database checksum when a user uploads a file.
If the file exists, it updates the database entry; otherwise, it creates a new entry. The Azure cloud will store the database and link the application to the cloud server via the internet. De-duplication reduces storage consumption, making it economical to handle in today’s tremendous data expansion.
This project aims to minimize duplicates in key-value stores, increase process performance so the backup window is minimally affected, and design with horizontal scaling in mind so it runs competitively on a Cloud Platform. As the project files and database file will be kept in Azure, the project will be accessible via Azure link in the web browser.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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