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How to Have Your Causality and Wall Clocks Too
Jon Moore talks about distributed monotonic clocks (DMC) whose timestamps can reflect causality but which have a component that stays close to wall clock time.
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Generics and Java's Evolution
Richard Warburton explains how to make effective use of Generics. Warburton sheds light on the planned changes in Java 10 using practical code examples at every step.
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Life of a Twitter JVM Engineer
Tony Printezis presents how services are deployed and monitored at Twitter, the benefits of using a custom-built JVM, and the challenges of the use of the JVM in an environment like Twitter.
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The Microservices and DevOps Journey
Aviran Mordo talks about how microservices and DevOps go hand in hand, and what it takes to operate and build a successful microservices architecture from development to production.
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Real-time Stream Computing & Analytics @Uber
Sudhir Tonse discusses using stream processing at Uber: indexing and querying of geospatial data, aggregation and computing of streaming data, extracting patterns, TimeSeries analyses and predictions.
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Open Source Swift Under the Hood
Alex Blewitt talks about Swift and looks at the open source project, how applications and libraries can be built, the differences between the different builds and how Swift works under the hood.
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Stream Processing with Apache Flink
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs.
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The Case for Bringing Swift to the Server
Patrick Bohrer and Chris Bailey present a preview of IBM latest cloud deployment configurations, Swift package-based cloud services, tools integration, and their plan to bring Swift to the server.
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Flying Faster with Heron
Karthik Ramasamy presents the design and implementation of Heron, the new de facto stream data processing engine at Twitter. Ramasamy shares Twitter’s experience of running Heron in production.
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Rethinking Streaming Analytics for Scale
Helena Edelson addresses new architectures emerging for large scale streaming analytics based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) or Apache Flink or GearPump.
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Connecting Stream Processors to Databases
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
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Broken Performance Tools
Brendan Gregg focuses on broken tools and metrics instead of the working ones. Metrics can be misleading, and counters can be counter-intuitive. He advises on how to approach new performance tools.
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