Data Streaming Architecture for Visualizing Cryptocurrency Temporal Data

The utilization of data streaming is becoming essential in mobile computing applications to reduce latency and increase bandwidth. Vast amounts of data are generated continuously from the Web sites of stock markets and financial institutions. The data’s meta-analysis is critical for investors and needs to analyze in a short time. Traditionally, it requires several heterogeneous resources with high storage capacity to process and compute the data. Data streaming helps to capture, pipeline, and compute the data without storing it. This research aims to visualize the continuous updates to the cryptocurrency temporal data using aggregations and simple response functions. The cryptocurrency data is collected from multiple data sources. A macro-enabled Excel external live data from Web feature, C3.js, and Tableau tools are used to capture and pipeline the streamed data in real time to make better decisions. The results show that the visualizations are dynamically updating in the events of trades in cryptocurrencies over time. Data streaming researchers and practitioners benefit from extending the streaming architecture methodology and dataflow to other domains.