Sustainably Supporting Data Variability
April 2015 • Presentation
Atzmon Hen-Tov (Pontis), Jordan Menzin (Boston Health Economics), Joseph Yoder (The Refactory, Inc.) Presenter Rebecca Wirfs-Brock
A challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. Three speakers share experiences.
Software Engineering Institute
This presentation was created for a conference series or symposium and does not necessarily reflect the positions and views of the Software Engineering Institute.
A big challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. In this session, three speakers—Atzmon Hen-Tov, a senior architect of a highly adaptable Telco platform; Jordan Menzin, architect of a Boston Health Economics’ health-care analytics system; and Joseph Yoder of The Refactory—share their experiences and hard-fought wisdom gained from building complex, data-intensive systems.
Invited Talk: Data Upgrade as a First-Class Citizen
Atzmon Hen-Tov explains how the ModelTalk system addresses data upgrade as an integral part of its product line architecture. Complex, large-scale business support systems in the telecommunication industry require high dependability while market pressures demand frequent releases. One aspect that hampers agility in a highly dependable system is data migration. In Pontis’ ModelTalk, an executable modeling framework, upgrades are first-class citizens, allowing for rapid evolution, agility, and reuse while at the same time supporting multiple persistency technologies.
Invited Talk: High-Performance Dynamic Health-Care Analytics
Jordan Menzin of Boston Health Economics (BHE) reviews the core architecture and key decisions that went into the creation of Instant Health Data, a highly performant health-care analytics system. Leveraging distributed computing and domain modeling, BHE has created an extensible platform that enables researchers to complete analytics projects using diverse data sources without the need for custom programming. This enables them to process large health-care data sets an order of magnitude faster than with legacy technologies.
Invited Talk: Keeping Core Components Clean While Dealing with Data Variability
Joseph Yoder of The Refactory examines strategies, practices, and patterns drawn from real experiences that support new and evolving data-processing requirements while keeping the core architecture clean. As complex systems evolve to meet varying data formats, they can devolve into poorly architected Big Balls of Mud filled with special-case logic and one-off processing. Alternatively, you can isolate core components of your system and protect them from entanglements and unnecessary complexity by designing them to operate on common data formats while providing extension mechanisms that enable processing variations.