Designing and Evolving Architectures for Big Data Applications

Big Data applications are playing critical role for all sorts of organisations. Whilst data scientist is considered a critical role for big data applications, the role of software architect has become even more critical as appropriate design and careful implementation are the key to successful big data applications for supporting organisational decisions and business processes. since this breed of applications are relatively new, the knowledge and experience for designing and evolving architectures for big data applications are in high demand. It is important that organisations build specific knowledge and competency in designing and evaluating software architectures for big data applications – this is one of the key areas where we have been focusing on R&D efforts for sometime now. We have started disseminating our work through different channels for seeking the application of our work through industrial partnerships. Recently, I gave a short talk on designing and evolving architectures for big data applications (Talk’s presentation) during the Adelaide Big Data Meet up called, Big Data Development. This talk took the knowledge-based approach to designing and evolving software architectures for big data systems. The talk highlighted the importance and mechanisms of building and leveraging architectural knowledge for designing and evolving data intensive applications. The talk also addressed the core issues that designers need to consider while designing data intensive applications and the support infrastructure required for developing competency in software design for data intensive applications. The talk presented the preliminary outcome from our ongoing R&D project on designing and evaluating architectures (Reference and institution of reference architectures) for domain specific (i.e., Defence and Smart Cities) big data applications. Now are looking for industrial partners for applying and evolving our work for specific industrial cases of designing and implementing knowledge-intensive solutions for designing and evaluating big data applications.