Download Hadoop: The Definitive Guide (4th Edition) by Tom White PDF

By Tom White

Prepare to release the facility of your information. With the fourth version of this finished consultant, you’ll the way to construct and keep trustworthy, scalable, dispensed structures with Apache Hadoop. This booklet is perfect for programmers trying to learn datasets of any measurement, and for directors who are looking to arrange and run Hadoop clusters.

Using Hadoop 2 completely, writer Tom White provides new chapters on YARN and several other Hadoop-related initiatives corresponding to Parquet, Flume, Crunch, and Spark. You’ll find out about fresh adjustments to Hadoop, and discover new case reviews on Hadoop’s function in healthcare platforms and genomics information processing.

• examine primary parts reminiscent of MapReduce, HDFS, and YARN
• discover MapReduce intensive, together with steps for constructing purposes with it
• arrange and preserve a Hadoop cluster operating HDFS and MapReduce on YARN
• examine information codecs: Avro for facts serialization and Parquet for nested data
• Use information ingestion instruments akin to Flume (for streaming information) and Sqoop (for bulk information transfer)
• know how high-level info processing instruments like Pig, Hive, Crunch, and Spark paintings with Hadoop
• research the HBase disbursed database and the ZooKeeper dispensed configuration carrier

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Hadoop: The Definitive Guide (4th Edition)

Prepare to unencumber the facility of your info. With the fourth variation of this complete consultant, you’ll how to construct and preserve trustworthy, scalable, disbursed structures with Apache Hadoop. This publication is perfect for programmers seeking to study datasets of any measurement, and for directors who are looking to arrange and run Hadoop clusters.

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Additional info for Hadoop: The Definitive Guide (4th Edition)

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MapReduce is a batch query processor, and the ability to run an ad hoc query against your whole dataset and get the results in a reasonable time is transformative. It changes the way you think about data and unlocks data that was previously archived on tape or disk. It gives people the opportunity to innovate with data. Questions that took too long to get answered before can now be answered, which in turn leads to new questions and new insights. For example, Mailtrust, Rackspace’s mail division, used Hadoop for processing email logs.

So, although it’s feasible to parallelize the processing, in practice it’s messy. Using a framework like Hadoop to take care of these issues is a great help. Analyzing the Data with Hadoop To take advantage of the parallel processing that Hadoop provides, we need to express our query as a MapReduce job. After some local, small-scale testing, we will be able to run it on a cluster of machines. Map and Reduce MapReduce works by breaking the processing into two phases: the map phase and the reduce phase.

For example, we can follow the number of records that went through the system: five map input records produced five map output records (since the mapper emitted one output record for each valid input record), then five reduce input records in two groups (one for each unique key) produced two reduce output records. The output was written to the output directory, which contains one output file per reducer. The job had a single reducer, so we find a single file, named part-r-00000: % cat output/part-r-00000 1949 111 1950 22 This result is the same as when we went through it by hand earlier.

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