Software Engineering Institute | Carnegie Mellon University
Software Engineering Institute | Carnegie Mellon University

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An Approach to Managing the Software Engineering Challenges of Big Data

  • “Testing these systems in all of the various ways that they may fail can be very difficult. So, it puts more importance on developing an architecture that satisfies good design principles in analyzing that architecture up front.
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    Blog | Addressing the Software Engineering Challenges of Big Data

  • Abstract

    In this episode, Ian Gorton and John Klein discuss big data and the challenges it presents for software engineers. With help from fellow SEI researchers, the two have developed a lightweight risk reduction approach to help software engineers manage the challenges of big data. Called Lightweight Evaluation and Architecture Prototyping (for Big Data), the approach is based on principles drawn from proven architecture and technology analysis and evaluation techniques to help the Department of Defense (DoD) and other enterprises including avionics, communications, and healthcare develop and evolve systems to manage big data.

  • Transcript

About the Speaker

  • Ian Gorton

    In his work at the SEI, Ian Gorton investigates issues related to software architecture at scale. This includes designing large-scale data management and analytic systems and understanding the inherent connections and tensions among software, data, and deployment architectures in cloud-based systems.

  • John Klein

    John Klein's research at the SEI focuses on developing design and analysis methods for systems-of-systems and helping individuals, teams, and organizations assess and improve their software architecture confidence.

  • Suzanne Miller

    Suzanne Miller

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How to Listen

This podcast is part of the SEI Podcast Series and is available to be played, downloaded, and more from the show page.

SEI Podcast Series: An Approach to Managing the Software Engineering Challenges of Big Data

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