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This article addresses the importance of HaaS (Hadoop-as-a-Service) in cloud technologies, with specific reference to its usefulness in big data mining for environmental computing applications. The term environmental computing refers to computational analysis within environmental science and management, encompassing a myriad of techniques, especially in data mining and machine learning. As is well-known, the classical MapReduce has been adapted within many applications for big data storage and information retrieval. Hadoop based tools such as Hive and Mahout are broadly accessible over the cloud and can be helpful in data warehousing and data mining over big data in various domains. In this article, we explore HaaS technologies, mainly based on Apache's Hive and Mahout for applications in environmental computing, considering publicly available data on the Web. We dwell upon interesting applications such as automated text classification for energy management, recommender systems for ecofriendly products, and decision support in urban planning. We briefly explain the classical paradigms of MapReduce, Hadoop and Hive, further delve into data mining and machine learning over the MapReduce framework, and explore techniques such as Naïve Bayes and Random Forests using Apache Mahout with respect to the targeted applications. Hence, the paradigm of Hadoop-as-a-Service, popularly referred to as HaaS, is emphasized here as per its benefits in a domain-specific context. The studies in environmental computing, as presented in this article, can be useful in other domains as well, considering similar applications. This article can thus be interesting to professionals in web technologies, cloud computing, environmental management, as well as AI and data science in general. 

The overconsumption of energy in recent times has motivated many studies. Some of these explore the application of web technologies and machine learning models, aiming to increase energy efficiency and reduce the carbon footprint. This paper aims to review three areas that overlap between the web and energy usage in the commercial sector: IoT (Internet of Things), cloud computing and opinion mining. The paper elaborates on problems in terms of their causes, influences, and potential solutions, as found in multiple studies across these areas; and intends to identify potential gaps with the scope for further research. In the rapidly digitizing and automated world, these three areas can offer much contribution towards reducing energy consumption and making the commercial sector more energy efficient. IoT and smart manufacturing can assist much in effective production, and more efficient technologies as per energy usage. Cloud computing, with reference to its impact on green IT (information technology), is a major area that contributes towards the mitigation of carbon footprint and the reduction of costs on energy consumption. Opinion mining is significant as per the part it plays in understanding the feelings, requirements and demands of the consumers of energy as well as the related stakeholders, so as to help create more suitable policies and hence navigate towards more energy efficient strategies. This paper offers comprehensive analyses on the literature in the concerned areas to fathom the current status and explore future possibilities of research across these areas and the related multidisciplinary avenues.