This report motivates the roles of Cloud computing, Edge computing, and thehierarchically distributed cooperative Fog computing, for the real-time analysis ofbig-data in Internets-of-Everything (IoEs). IoEs are enhanced Internets of Things (IoTs)which integrate people, process, data and heterogeneous "Things": compute, storage,and sensor/actuator hardware. The ubiquitousness of IoE devices, the ever-increasingamount of big-data in IoEs, and the need for real-time computing in IoEs have motivatedthe problem of distributed data storage and analysis. With trillions (big-data scale) of IoEdevices on the verge of being deployed in tomorrow’s ever-connected and autonomoussociety, and with the expected big-data generated by each such IoE device (typicallyimage data of the order of tens and hundreds of gigabytes per day), we are rapidlyapproaching Big-Squared data dimensions. The power consumption of traditional clouddata centers are already about 70% of all power generated, and it will increaseexponentially if Cloud computing is the only solution for tomorrow’s IoEs. Moreover, theBig-Squared-Data from merging IoEs will create network and compute level bottlenecksthat will be impractical from a real-time standpoint, especially in case of rapid mobility inIoEs. Hence, the need for distributed hierarchical Fog computing and associated datamanagement. I survey key features of emerging IoEs, the existing big-data computingand storage frameworks, and point out their capabilities and deficiencies. I discuss thedesign and implementation of my Fog computing architecture. I present my work in Accident Identification and Related Congestion Control. The results show that having aFog Computing Layer helps in cutting down data bandwidth to the cloud and reducestotal latency by approximately 80%. Finally, I have shown that the integration of ApacheKafka, Apache Cassandra and Spark Streaming in a Fog Computing Architecture has agreater impact on the problem at hand and it is successful in solving both the issues ofreal-time and near-real-time responses along with containing the Big-Squared-Data tojust big-data and conserving the data bandwidth.