While big data techniques such as log based systems offer robust and scalable state management, crawling data at scale is not without its challenges. This presentation offers an approach developed for managing the state in a master data management system using MongoDB that facilitates accelerated traversal of a multi-dimension time series through the use of auxiliary information, in addition to improving the overall utility of the data. While this approach was developed specifically for master data management, it may be applicable more broadly to other applications managing batch oriented state at scale.
This presentation may be of interest to any practitioner considering adopting and advancing non-CRUD based state management across their firms.
Neil Boddy From Goldman Sachs
I am an engineer passionate about software design, methods, techniques and architecture with many years experience leading and developing enterprise scale applications and platforms. I am particularly interested in finding solutions to challenging business problems that not only meet the business needs but are also underpinned with the conditions that improve system sustainability and developers quality of life.
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