The on-going growth in information and technology sector has increased storage requirement in cloud data centres with unprecedented pace. Global storage reached 2.8 trillion GB as per EMC Digital Universe study 2012 and will reach 5247GB per user by 2020. Data redundancy is one of the root factors in storage scarcity because clients upload data without knowing the content available on the server. Ponemon Institute detected 18% redundant data in “National Survey on Data Centers Outages” . To resolve this issue, the concept of data deduplication is used, where each file has a unique hash identifier that changes with the content of the file. If a client tries to save duplicate of an existing file, he/she receives a pointer for retrieving the existing file. In this way, data DE duplication helps in storage reduction and Identifying redundant copies of the same files stored at data centres. Therefore, many popular cloud storage vendors like Amazon, Google Drop box, IBM Cloud, Microsoft Azure, Spider Oak, Walla and Mazy adopted data DE duplication. In this study, we have made a comparison of commonly used File-level DE duplication with our proposed Block-level DE duplication for cloud data centres. We implemented the two DE duplication approaches on a local dataset and demonstrated that the proposed Block-level DE duplication approach shows 5% better results as compared to the File-level DE duplication approach. Furthermore, we expect that the performance will further be improved by considering a large dataset with more users working in similar domain.