800ˤ&ܴdz;ʳ٤&ܴdz;ᲹǴDZǷɢI
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Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the distributed processing of large datasets across clusters of nodes using simple programming models. Hadoop is highly scalable, running on thousands of nodes, and is designed to reliably handle failures at the hardware or software level.
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The document summarizes the Hadoop Conference Japan that took place in fall 2011. It discusses topics such as MapReduce, Hive, Pig, Asakusa frameworks, REST APIs, Elastic MapReduce on AWS, and new features in MapReduce2. The conference also covered Hadoop usage scenarios including log analysis, web analytics, and managing large datasets exceeding 100 terabytes.Big Data - Lab A1 (SC 11 Tutorial)
Big Data - Lab A1 (SC 11 Tutorial)Robert Grossman
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This document provides an introduction and overview of using Amazon's Elastic MapReduce (EMR) service for data intensive computing. It discusses uploading data to S3 storage, writing mappers and reducers in various languages like Python and streaming utilities, and executing a MapReduce job on EMR to process the data in parallel across a cluster of Amazon EC2 instances. The key steps involve loading input data to S3, defining the mapper and reducer processing logic, and downloading outputs from S3 upon job completion.Hadoop - Lessons Learned
Hadoop - Lessons Learnedtcurdt
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This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.Hadoop Family and Ecosystem
Hadoop Family and Ecosystemtcloudcomputing-tw
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This document provides an overview of Hadoop and its ecosystem. It describes Hadoop as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage, and MapReduce as a programming model for distributed computation across large datasets. A variety of related projects form the Hadoop ecosystem, providing capabilities like data integration, analytics, workflow scheduling and more.Hadoop Overview kdd2011
Hadoop Overview kdd2011Milind Bhandarkar
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The document provides an extensive overview of modeling with Hadoop, covering its architecture, features, and practical applications. It details sessions on Hadoop fundamentals, data processing with MapReduce, and hands-on exercises for model training. The document highlights Hadoop's scalability, distributed file system, and challenges in large data processing, emphasizing both its growth and evolving capabilities.Hadoop Overview & Architecture
Hadoop Overview & Architecture EMC
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This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.Ad
More from Tatsuya Sasaki (6)
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ץߥǶٳ餳Tatsuya Sasaki
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The document defines a User class with attributes for name and age that has many accounts. It then opens a CSV file, splits each line into an ID, name, and age, and appends them to an array. If the array reaches over 1000 items, an insert is executed. It also defines a parse method that takes a filepath and separator, iterates over each line split on the separator, and yields to a block.Ad
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