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Spring Projects Infrastructure
Roy Clarkson and Gunnar Hillert present how GitHub, Gradle, and Artifactory are used to upgrade Spring’s infrastructure and processes.
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F# for Trading
Phil Trelford describes and demonstrates areas where F# excels, such as domain modeling, computation and concurrency.
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Functional DSLs for Biocomputation
Colin Gravill talks about how using F# to construct a shared analysis engine and the languages used to make the individual tools.
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Getting started with Spring Data and Distributed Database Grids
Mark Johnson and David Turanski introduce Spring Data for GemFire demoing using Spring Data for persistency across multiple distributed database grids.
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Actionable Metrics - Enabling Decision-Making in Netflix’s Decentralized Environment
Roy Rapoport discusses how Netflix uses metrics to monitor and manage their operating environment along with some notes about their event management system.
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SPDY, err... HTTP 2.0: What Is It, How, Why, and When?
Roberto Peon introduces SPDY which is the starting point for HTTP 2.0, a standard in development, explaining why a new HTTP standard is needed and how SPDY helps.
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Building a Reliable Data Store
Jeremy Edberg presents the data stores used by Netflix and Reddit, some of the best practices and lessons for surviving outages.
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An Overview of Guava: Google Core Libraries for Java
Kevin Bourrillion introduces Guava, a set of open source core libraries used internally by Google.
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Building Google Cloud Storage
Nathan Herring presents the available storage options at Google, the ideal characteristics of a storage service, and the actual implementation of Google Cloud Storage.
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Data Modeling with Graphs
Peter Bell presents several patterns for modeling and retrieving data from graph databases using Neo4j in his examples.
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Ten Reasons Why You Should Use a Graph Database For Your CMS
Axel Morgner compares different open source CMS’s and outlines the benefits of implementing one using a graph database.
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Reverend Bayes, Meet Countess Lovelace: Machine Learning and Programming
Andy Gordon discusses machine learning using functional programming, explaining how Infer.NET Fun turns the succinct syntax of F# into an executable modeling language for Bayesian machine learning.
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