Discussion Responses

Discussion 1: Sensors are used to identify specific data and respond to input. They do this from a physical environment. And the output is used to generate information for a process or system. Discussion1: Because global computing and big data are about accuracy and speed, sensors accelerate the process and generate accurate big data processes. Monitoring is crucial in both global processing and big data algorithms. Sensors are used to monitor processes in global processing reliably. The productivity of big data and global computing processes has increased with sensors. They have also helped reduce the cost of ownership in computing environments. There is less power consumption with the sensors. Computer networks have provided the means to connect different hosts and servers in big data and global computing. Applications differ in additional requirements, but computer networks have provided the standards for other systems to work together and share data. This is the essential requirement for both global processing and big data. This is why data processing around the world. Because there are computer networks that connect different computer devices worldwide, they also provide good security with the help of firewalls, etc. Big data uses networks to build and generate large sets of data sets. This helps in the execution of the algorithm. Data storage for big data and global processing consists of infrastructure, storage devices, and mechanisms to store, retrieve, process, and manage large amounts of data. Without data storage, none of these technologies exist. Both depend on the data and the value derived from the data. A clustered computer system provides ways for nodes and computers on the network to work together. They can be near or loosely related. However, they are seen as a system. Different clusters assign their nodes to the same task. The efficiency of global computer systems and big data processes increases. Cloud computing is the foundation of the infrastructure needed for big data and global computing. The user collects information or data from the cloud and extracts the data from it. It helped reduce costs, increase scalability, and maintain effective collaboration. Data analysis algorithms have made big data, and the global data processing process faster. It's easy to make quick decisions with facts and figures. Awareness of security problems and risks has increased with the number of security measures. References: Khan, N., Yaqoob, I., Hashem, I., Inayat, Z., Mahmoud Ali, W., Alam, M., . . . Gani, A. (2014, July 17). Big Data: Survey, Technologies, Opportunities, and Challenges. Retrieved from https://www.hindawi.com/journals/tswj/2014/712826/ Big Data Analytics - What it is and why it matters. (n.d.). Retrieved from https://www.sas.com/en_us/insights/analytics/big-data-analytics.html Discussion 2: There are many technologies that can be used for big data and also global computing. Sensors are used to monitor the cloud networks and with the current development of Wireless Sensor Network (WSN), various industries have been using the systems to improve their services (Alamri et al., 2013). The sensors can be integrated with the IT infrastructure to improve user experience and also the effectiveness of information security. The data storage systems have also been improved as big data becomes sophisticated. The digital platform has enabled companies to collect and store huge volumes of data. Using the cloud computing systems, this information can be retrieved when necessary. The computer networks give the computing environment the ability to share resources . Within the organizations, computers are normally connected via the internet or even a file printer. The internet provides the opportunity for people to share files or ideas. The cluster computing systems can be used to provide high performance through the computing environment. The machines will be able to function as a single unit through nodes. There is normally high performance associated with big data. The clusters are key in reducing the downtime and also the outages in the systems (Tannahill & Jamshidi, 2014). There are various cloud computing facilities that aim at effectively handling big data. They include Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and also Software as a Service (SaaS). These models are efficient in managing the fluctuating data demands and operations. In handling big data, various data analysis algorithms such as machine learning algorithms have led to handling of complex issues (Hashem et al., 2015). References Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A., & Hossain, M. A. (2013). A survey on sensor-cloud: Architecture, applications, and approaches. In International Journal of Distributed Sensor Networks (Vol. 2013). Hindawi Publishing Corporation. https://doi.org/10.1155/2013/917923 Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. In Information Systems (Vol. 47, pp. 98–115). Elsevier Ltd. https://doi.org/10.1016/j.is.2014.07.006 Tannahill, B. K., & Jamshidi, M. (2014). System of Systems and Big Data analytics - Bridging the gap. Computers and Electrical Engineering, 40(1), 2–15. https://doi.org/10.1016/j.compeleceng.2013.11.016