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Oil industries generate an enormous volume of digitized data (e.g., seismic data) as a part of their seismic study and move it to the cloud for downstream applications. Moving massive data into the cloud can pose many challenges, especially to Commercial-off-the-shelf geoscience applications as they require very high compute and disk throughput. This paper proposes a digital transformation framework for efficient seismic data processing and storage comprising of: (a) Novel Data storage options, (b) Cloud-based HPC framework for efficient seismic data processing, and (c) MD5 hash calculation using the MapReduce pattern with Hadoop clusters. Azure cloud platform is used to validate the proposed framework and compare it with the existing process. Experimental results show a significant improvement in execution time, throughput, efficiency, and cost. The proposed framework can be used in any domain which deals with extensive data requiring high compute and throughput.