An End-to-End Big Data Application Architecture for The Common Tactical Picture

Abstract

Project Summary: Big data is opening up new opportunities for organizations to gain insight and make intelligent inferences from huge volumes of data in real time and across multiple data sources. All these disparate sources of data need to be managed in a consolidated and integrated manner to get valuable inferences and insights. The architectures for realizing these opportunities are based on relatively inexpensive and heterogeneous infrastructures than the traditional monolithic and expensive options that exist currently. Selecting the right architecture is crucial to harnessing the power of big data. However, heterogeneity brings with it multiple options for solving the same problem, as well as the need to evaluate trade-offs and validate the “fitness-for-purpose” of the solution. The goal of this research is to propose an end-to-end application architecture for the Common Tactical Picture and Combat ID that ensures that all essential components required to collect, store, analyze, and visualize all aspects of the big data set are present. The elements of the proposed architecture include a data sources layer, an ingestion layer, a storage layer, a physical infrastructure layer, a platform management layer, an analytics engine layer, a visualization layer, a security layer, and a monitoring layer.