InfoQ Homepage Case Study Content on InfoQ
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Incident Management at the Edge
Lisa Phillips discusses the typical struggles a company runs into when building around-the-clock incident operations and the things Fastly has put in place to make dealing with incidents easier.
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Data Science in the Cloud @StitchFix
Stefan Krawczyk discusses how StitchFix used the cloud to enable over 80 data scientists to be productive and have easy access, covering prototyping, algorithms used, keeping schema in sync, etc.
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Petabytes Scale Analytics Infrastructure @Netflix
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
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Automatic Discovery of Service Metadata for Systems at Scale
Martina Iglesias Fernández discusses Spotify’s approach to documentation through automatic discovery of existing endpoints, service configuration, and deployment information at runtime.
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Scaling the Data Infrastructure @Spotify
Mārtiņš Kalvāns and Matti Pehrs overview the Data Infrastructure at Spotify, diving into some of the data infrastructure components, such us Event Delivery, Datamon and Styx.
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Spring Data Hazelcast: Fluently Accessing Distributed Repositories
Victor Gamov and Neil Stevenson present using Spring Data for a Hazelcast project, built on the KeyValue module and providing infrastructure components for creating repository abstractions.
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How We Work Remotely at Particular Software
Don Belcham shares from his experience working for a company where everybody works remotely, what they do about meetings, how collaboration works, and how it compares with a regular company.
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Scaling Counting Infrastructure @Quora
Chun-Ho Hung and Nikhil Garg discuss Quanta, Quora's counting system powering their high-volume near-real-time analytics, describing the architecture, design goals, constraints, and choices made.
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Kanban Metrics in Practice
Mattia Battiston shares from his experience at Sky Network: what metrics they use, how they use them, what pitfalls they encountered and what little data they collect to get a whole lot of value.
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Scaling Quality on Quora Using Machine Learning
Nikhil Garg talks about the various Machine Learning problems that are important for Quora to solve in order to keep the quality high at such a massive scale.
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Iterative Design for Data Science Projects
Bo Peng goes over how Datascope iterated on the major pieces of the Expert Finder application project to produce actionable insights and recommendations on methodologies.
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Instant and Personal: Searching Your Network at LinkedIn
Shakhina Pulatova overviews the Instant Search experience at LinkedIn and how they use Machine Learning to deliver personalized results as the query is typed.
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