InfoQ Homepage MapReduce Content on InfoQ
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Hydrator: Open Source, Code-Free Data Pipelines
Jonathan Gray introduces Hydrator, an open source framework and user interface for creating data lakes for building and managing data pipelines on Spark, MapReduce, Spark Streaming and Tigon.
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Translating Imperative Code to MapReduce
The authors present an approach for automatic translation of sequential, imperative code into a parallel MapReduce framework using Mold, translating Java code to run on Apache Spark.
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Hadoop 201 -- Deeper into the Elephant
Roman Shaposhnik discusses more advanced features of HDFS, in addition to how YARN has enabled businesses to massively scale their systems beyond what was previously possible.
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Why Spark Is the Next Top (Compute) Model
Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.
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Spring XD for Real-time Hadoop Workload Analysis
The authors explain how the Pivotal team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization using Spring XD.
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Getting Real with the MapR Platform
Jim Scott keynotes on the history of Hadoop, the difficulties that this technology has gone through, exploring the reasons why enterprises need to evaluate their targets and prepare for the future.
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JS Optimization Techniques
Guillaume Lathoud suggests expanding JavaScript with mutual tail-call optimization, map/filter/reduce and math computations to obtain faster code.
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Scaling Pinterest
Details on Pinterest's architeture, its systems -Pinball, Frontdoor-, and stack - MongoDB, Cassandra, Memcache, Redis, Flume, Kafka, EMR, Qubole, Redshift, Python, Java, Go, Nutcracker, Puppet, etc.
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REEF: Retainable Evaluator Execution Framework
Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.
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Exercises in Style
Crista Lopes writes a program in multiple styles -monolithic/OOP/continuations/relational/Pub-Sub/Monads/AOP/Map-reduce- showing the value of using more than a style in large scale systems.
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MapReduce and Its Discontents
Dean Wampler discusses the strengths and weaknesses of MapReduce, and the newer variants for big data processing: Pregel and Storm.
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Wrap Your SQL Head Around Riak MapReduce
Sean Cribbs explains what Map-Reduce and Riak are, why and how to use Map-Reduce with Riak, and how to convert SQL queries into their Map-Reduce equivalents.
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