Architectural Approaches for Data-Intensive Scenarios
Nowadays, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate at which it changes, and the heterogeneity of formats. To address this challenge, software architects resort to build complex data-intensive architectures that integrate highly heterogeneous software systems, each offering vertically specialized functionalities. Designing a suitable architecture for the application at hand is crucial to enable high quality of service and efficient exploitation of resources. It entails a series of decisions that demand technical expertise and in-depth knowledge of individual systems and their synergies.To assist software architects, this research aims to identify strategies to address the various challenges they may encounter, with the goal of automating the design and deployment of data-intensive architectures.The goal is to identify strategies to simplify the design and development of distributed data-intensive architectures, applying software engineering techniques in this specific context and trying to automate the entire process through systematic methodologies.
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