Big Data Without Big Database—Extreme In-Memory Caching for Better Performance with Kate Matsudaira
These days it is not uncommon to have 100s of gigabytes of data that must be sliced and diced, then delivered fast and rendered quickly. Typically solutions involve lots of caching and expensive hardware with lots of memory. And, while those solutions certainly can work, they aren't always cost-effective, or feasible in certain environments (like in the cloud). This talk seeks to cover some strategies for caching large data sets without tons of expensive hardware, but through software and data design. It's common wisdom that serving your data from memory dramatically improves application performance and is a key to scaling. However, caching large datasets brings its own challenges: distribution, consistency, dealing with memory limits, and optimizing data loading just to name a few. This talk will go through some of the challenges, and solutions, to achieve fast data queries in the cloud. The audience will come away armed with a number of practical techniques for organizing and building caches for non-transactional datasets that can be applied to scale existing systems, or design new systems.
Kate Matsudaira specializes in creating and operating large-scale web applications. Her focus has primarily rested on SaaS applications and big data. She has extensive experience building and managing high-performance teams, and considers herself a fan of agile development practices and the lean startup movement. Kate is currently founding her own startup, popforms, but has held roles as developer, project manager, product manager, and people manager at companies including Amazon and Microsoft. The last seven years she has been a VP of Engineering/CTO for companies like Moz, Decide (acquired by eBay), and prior to that Delve Networks (acquired by Limelight). She is also one of the curators of the Technology and Leadership Newsletter (TLN). You can follow Kate's blog to see her writings on tech and leadership.