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details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text;
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As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis;
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As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis;
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The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have;
Vergelijkbare producten zoals Big Data with Hadoop MapReduce
The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have;
Vergelijkbare producten zoals Big Data with Hadoop MapReduce
process large amounts of data offline. The book begins with several simple examples that illustrate the basic idea behind Hadoop;
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Advances in remote sensing technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms these;
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projects on Hadoop. With Pig, you can analyze data without having to create a full-fledged application - making it easy for you to experiment with;
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solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining;
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), then run distributed computations with MapReduce * Use Hadoop's data and I/O building blocks for compression, data integrity, serialization;
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network, Google initiated MapReduce model. This model is an implementation of Parallel Computing, which aims at processing large amount of data;
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When dealing with big data, traditional databases don't cut it. It's best to use the right tool for the job. HBase is a NoSQL storage;
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This book is aimed at developers and system administrators who want to learn about Big Data analysis using Amazon Elastic MapReduce. Basic;
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the Scalding framework. It is packed with examples featuring log-processing, ad-targeting, and machine learning. This book is for developers;
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application development and portability with applications in cloud computing and big data. You'll use MapReduce and other, similar big data tools as;
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, virtual 4-node Linux YARN cluster on a Windows laptop. Rather than filled with disjointed, piecemeal code snippets to show Hadoop MapReduce;
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The last decade has seen a huge and growing interest in processing large data sets on large distributed clusters. This trend began with the;
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Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult;
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, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a;
Vergelijkbare producten zoals An Architecture for Fast and General Data Processing on Large Clusters
, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a;
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familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence * Discover common pitfalls and;
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Ready to unlock the power of your data? With the fourth edition of this comprehensive guide, you'll learn how to build and maintain;
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Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various;
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Hadoop to get useful insights from large data sets Work with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the;
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write data processing programs in Python that are highly available, reliable, and fault tolerant * Make use of Amazon Web Services along with;
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