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  • Book Overview & Buying Mastering Hadoop
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Mastering Hadoop

Mastering Hadoop

By : Karanth
4 (3)
Buy this Book
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Mastering Hadoop

Mastering Hadoop

4 (3)
By: Karanth
Buy this Book

Overview of this book

Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book?
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Hadoop 2.X
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1. Hadoop 2.X
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The inception of Hadoop
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The evolution of Hadoop
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Hadoop 2.X
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Hadoop distributions
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Summary
2
2. Advanced MapReduce
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2. Advanced MapReduce
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MapReduce input
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The RecordReader class
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Hadoop's "small files" problem
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Filtering inputs
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The Map task
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The Reduce task
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MapReduce output
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MapReduce job counters
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Handling data joins
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Summary
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3. Advanced Pig
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3. Advanced Pig
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Pig versus SQL
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Different modes of execution
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Complex data types in Pig
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Compiling Pig scripts
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Development and debugging aids
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The advanced Pig operators
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User-defined functions
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Pig performance optimizations
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Best practices
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Summary
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4. Advanced Hive
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4. Advanced Hive
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The Hive architecture
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Data types
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File formats
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The data model
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Hive query optimizers
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Advanced DML
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UDF, UDAF, and UDTF
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Summary
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5. Serialization and Hadoop I/O
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5. Serialization and Hadoop I/O
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Data serialization in Hadoop
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Avro serialization
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File formats
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Compression
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Summary
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6. YARN – Bringing Other Paradigms to Hadoop
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6. YARN – Bringing Other Paradigms to Hadoop
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The YARN architecture
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Developing YARN applications
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Monitoring YARN
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Job scheduling in YARN
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YARN commands
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Summary
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7. Storm on YARN – Low Latency Processing in Hadoop
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7. Storm on YARN – Low Latency Processing in Hadoop
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Batch processing versus streaming
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Apache Storm
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Storm on YARN
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Summary
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8. Hadoop on the Cloud
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8. Hadoop on the Cloud
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Cloud computing characteristics
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Hadoop on the cloud
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Amazon Elastic MapReduce (EMR)
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Summary
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9. HDFS Replacements
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9. HDFS Replacements
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HDFS – advantages and drawbacks
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Amazon AWS S3
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Implementing a filesystem in Hadoop
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Implementing an S3 native filesystem in Hadoop
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Summary
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10. HDFS Federation
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10. HDFS Federation
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Limitations of the older HDFS architecture
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Architecture of HDFS Federation
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HDFS high availability
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HDFS block placement
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Summary
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11. Hadoop Security
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11. Hadoop Security
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The security pillars
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Authentication in Hadoop
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Authorization in Hadoop
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Data confidentiality in Hadoop
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Audit logging in Hadoop
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Summary
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12. Analytics Using Hadoop
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12. Analytics Using Hadoop
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Data analytics workflow
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Machine learning
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Apache Mahout
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Document analysis using Hadoop and Mahout
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RHadoop
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Summary
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A. Hadoop for Microsoft Windows
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A. Hadoop for Microsoft Windows
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Deploying Hadoop on Microsoft Windows
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Summary
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Index
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Index

Machine learning

Machine learning is about programming computers to optimize a function based on previous experience. The computer is given empirical data to analyze and build a model function that can predict the output on unseen data that it might encounter in the real world. The computer builds a function based on the parameters and the empirical data supplied to it. This function evolves as more empirical data is given or when there is a change in the data characteristics. When this function is applied on unseen data at a later point, it predicts the output based on the model function. The empirical data supplied to learn this function is termed as training data.

The following are the kinds of machine learning algorithms:

  • Supervised learning: The training data supplied to supervised learning methods is labeled. Each data point in the training dataset is a pair of objects, the actual data point representing the situation, which is generally a vector of values, and the desired output value...
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Tech Concepts
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Programming languages
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Mastering Hadoop
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