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    Apache Flume: Distributed Log Collection for Hadoop. If your role includes moving datasets into Hadoop, this book will help you do it more efficiently using Apache Flume. From installation to customization, it's a complete step-by-step guide on making the service work for you

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    Apache Flume: Distributed Log Collection for Hadoop. If your role includes moving datasets into Hadoop, this book will help you do it more efficiently using Apache Flume. From installation to customization, it's a complete step-by-step guide on making the service work for you Steve Hoffman, Steven Hoffman, Kevin A. McGrail - okładka ebooka

    Apache Flume: Distributed Log Collection for Hadoop. If your role includes moving datasets into Hadoop, this book will help you do it more efficiently using Apache Flume. From installation to customization, it's a complete step-by-step guide on making the service work for you Steve Hoffman, Steven Hoffman, Kevin A. McGrail - okładka ebooka

    Apache Flume: Distributed Log Collection for Hadoop. If your role includes moving datasets into Hadoop, this book will help you do it more efficiently using Apache Flume. From installation to customization, it's a complete step-by-step guide on making the service work for you Steve Hoffman, Steven Hoffman, Kevin A. McGrail - okładka audiobooka MP3

    Apache Flume: Distributed Log Collection for Hadoop. If your role includes moving datasets into Hadoop, this book will help you do it more efficiently using Apache Flume. From installation to customization, it's a complete step-by-step guide on making the service work for you Steve Hoffman, Steven Hoffman, Kevin A. McGrail - okładka audiobooks CD

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    Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Its main goal is to deliver data from applications to Apache Hadoop's HDFS. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with many failover and recovery mechanisms.

    Apache Flume: Distributed Log Collection for Hadoop covers problems with HDFS and streaming data/logs, and how Flume can resolve these problems. This book explains the generalized architecture of Flume, which includes moving data to/from databases, NO-SQL-ish data stores, as well as optimizing performance. This book includes real-world scenarios on Flume implementation.

    Apache Flume: Distributed Log Collection for Hadoop starts with an architectural overview of Flume and then discusses each component in detail. It guides you through the complete installation process and compilation of Flume.

    It will give you a heads-up on how to use channels and channel selectors. For each architectural component (Sources, Channels, Sinks, Channel Processors, Sink Groups, and so on) the various implementations will be covered in detail along with configuration options. You can use it to customize Flume to your specific needs. There are pointers given on writing custom implementations as well that would help you learn and implement them.

    By the end, you should be able to construct a series of Flume agents to transport your streaming data and logs from your systems into Hadoop in near real time.

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    O autorach ebooka

    Steve Hoffman has 32 years of experience in software development, ranging from embedded software development to the design and implementation of large-scale, service-oriented, object-oriented systems. For the last 5 years, he has focused on infrastructure as code, including automated Hadoop and HBase implementations and data ingestion using Apache Flume. Steve holds a BS in computer engineering from the University of Illinois at Urbana-Champaign and an MS in computer science from DePaul University. He is currently a senior principal engineer at Orbitz Worldwide (https://orbitz.com/). More information on Steve can be found at https://bit.ly/bacoboy and on Twitter at @bacoboy. This is the first update to Steve's first book, Apache Flume: Distributed Log Collection for Hadoop, Packt Publishing.
    Steve Hoffman has 32 years of experience in software development, ranging from embedded software development to the design and implementation of large-scale, service-oriented, object-oriented systems. For the last 5 years, he has focused on infrastructure as code, including automated Hadoop and HBase implementations and data ingestion using Apache Flume. Steve holds a BS in computer engineering from the University of Illinois at Urbana-Champaign and an MS in computer science from DePaul University. He is currently a senior principal engineer at Orbitz Worldwide (https://orbitz.com/). More information on Steve can be found at https://bit.ly/bacoboy and on Twitter at @bacoboy. This is the first update to Steve's first book, Apache Flume: Distributed Log Collection for Hadoop, Packt Publishing.

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