InfoQ Homepage Paypal Content on InfoQ
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Reactor: The New Power Source for PayPal's JVM Framework
Anil Gursel and Rick Hogge share the technical journey for moving PayPal engineers from a JAX-RS imperative programming model to a reactive stack.
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Scaling DB Access for Billions of Queries Per Day @PayPal
Petrica Voicu and Kenneth Kang talk about Hera (High Efficiency Reliable Access to data stores) – an open-source in Go – and how it helps PayPal to manage database access and deal with issues.
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Bringing JAMStack to the Enterprise
Jamund Ferguson talks about some of the challenges PayPal faced with their Node.js application servers, why they think the JAMStack approach improves performance for their apps and their developers.
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On a Deep Journey towards Five Nines
Aashish Sheshadri discusses how PayPal applies Seq2Seq networks to forecasting CPU and memory metrics at scale.
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Massive Scale Anomaly Detection Framework
Guy Gerson introduces an anomaly detection framework PayPal uses, focusing on flexibility to support different types of statistical and ML models, and inspired by scikit-learn and Spark MLlib.
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CRDTs in Production
Dmitry Martyanov talks about how PayPal developed a distributed system dealing with consistency issues and shares lessons learned in developing the system based on an eventually consistent data store
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ML Data Pipelines for Real-Time Fraud Prevention @PayPal
Mikhail Kourjanski focuses on the architectural approach towards PayPal’s real-time service platform that leverages ML models, delivers performance and quality of decisions.
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Gimel: PayPal’s Analytics Data Platform
Deepak Chandramouli introduces and demos Gimel, a unified analytics data platform which provides access to any storage through a single unified data API and SQL.
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Real-Time Data Analysis and ML for Fraud Prevention
Mikhail Kourjanski addresses the architectural approach towards the PayPal internally built real-time service platform, which delivers performance and quality of decisions.
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Data Pipelines for Real-Time Fraud Prevention at Scale
Mikhail Kourjanski discusses the architecture of PayPal’s data service which combines a Big Data approach with providing data in real time for decision making in fraud detection.
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Large Scale Machine Learning for Payment Fraud Prevention
Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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Spring Boot @ PayPal
Fabio Carvalho and Eduardo Solis from PayPal discuss adding Spring Boot to their RESTful Java framework providing a microservices architecture based on cloud, CI, Docker and embedded containers.
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