With the gradual growing concern for the safe environment, the importance of the air quality inside buildings, office spaces, and so on has drawn significant concern as the deterioration in the same has led to health problems among the occupants. Literature shows that the total economic cost, combining both the direct medical costs and productivity losses on account of contaminated indoor air, has been found to be around $5 billion. Further, it has been shown that there are two types of illness-related effects. The first is related to building-related diseases, and the second is related to non-specific building-related illnesses, which are universally acknowledged as Sick Building Syndrome. To achieve the same surveillance for the presence and identification of their ill effect on occupants of the building, a significant amount of data have been physically and electronically collected, collated and analyzed along with the institution of appropriate preventive as well as rectification measures. The present study is a descriptive study that is aimed to collect significant data regarding indoor air quality from a randomly selected corporate office or data available in the public domain from the Internet and then to apply suitable compression technique among available techniques to the Big Data so obtained. A ubiquitous data compression technique, Snappy, has been used and compared with another commonly used compression technique, “ORC.” The big data source file has been obtained from the public domain. Also, the key concept of cloud computing has been learnt and understood to apply it to big data processing and management.