In India, because of the fluctuations in Internet bandwidth rate, data loss occur most of the times. Cloud computing is the latest technology to handle the data through multiple devices. In this paper we have implemented an idea to store and access the data through the cloud depending on Internet bandwidth speed. To achieve this, we have used data compression technique which mainly uses SQL server. In this technique we compress the data and store it separately in cloud server. The compressed data uses only 5% of original data, and it doesn’t create a storage problem. Depending on connection type like mobile broadband, Wi-Fi and DSL, speed of the internet is calculated. With this we have used fault tolerance to handle large amount of data and data encapsulation to secure the user data. We have used Map-Reduce programming to lossless data compression and handle Big- Data. Our solution builds on a highly parallel distributed data management service specially designed to enable reading, writing and appending huge data sequences that are fragmented and distributed at a large scale. This paper focuses on evaluating the benefits of applying data compression transparently at the level of the storage service in the context of data-intensive applications.

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