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In the above screenshots, it is shown using Lambda, a 3-MB .jpg file is compressed to 9.8 KB in a couple of seconds. In our proposed model, if a user accounts for the data transfer of 51.2 MB per day, then at an average, about 1,000 users are responsible for uploading about 50 GB of data per day. That number turns to 1,500 GB of object-based data saved by the end of a month. Figure 1.13 shows before compression of data (raw data), and Figure 1.14 shows after compression of data (compressed data). Due to the huge amount of data that needs to be handled, it is highly efficient to compress the data, keeping it intact to reduce costs on long term storages and cost incurred on egress traffic. By the method implemented in our proposed model, the 50 GB of data per day can be compressed into as less as 125 MB per day. It is almost a 500% decrease in the data size, saving costs to a great extent, making our proposed model much more economical.

Figure 1.13 Before compression of data (raw data).

Figure 1.14 After compression of data (compressed data).