InfoQ Homepage Big Data Content on InfoQ
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Big-Data Analytics Misconceptions
Irad Ben-Gal discusses Big Data analytics misconceptions, presenting a technology predicting consumer behavior patterns that can be translated into wins, revenue gains, and localized assortments.
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How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
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The Mechanics of Testing Large Data Pipelines
Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.
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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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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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Developing Real-time Data Pipelines with Apache Kafka
Joe Stein makes an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log.
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Apache Spark for Big Data Processing
Ilayaperumal Gopinathan and Ludwine Probst discuss Spark and its ecosystem, in particular Spark Streaming and MLlib, providing a concrete example, and showing how to use Spark with Spring XD.
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The Lego Model for Machine Learning Pipelines
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
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Tuning Java for Big Data
Scott Seighman discusses causes of common performance issues in Big Data environments, heap size, garbage collection, JVM reuse tuning guidelines and Big Data performance analysis tools.
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Ground-up Introduction to In-memory Data
Viktor Gamov covers In-Memory technology, distributed data topologies, making in-memory reliable, scalable and durable, when to use NoSQL, and techniques for Big In-Memory Data.
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Pulsar: Real-time Analytics at Scale
Sharad Murthy & Tony Ng present Pulsar, a real-time streaming system which can scale to millions of events per second with high availability and 4GL language support.
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Exploratory Data Analysis with R
Matthew Renze introduces the R programming language and demonstrates how R can be used for exploratory data analysis.