Books & Videos

Table of Contents

  1. Hadoop Fundamentals

    1. Chapter 1 Meet Hadoop

      1. Data!
      2. Data Storage and Analysis
      3. Querying All Your Data
      4. Beyond Batch
      5. Comparison with Other Systems
      6. A Brief History of Apache Hadoop
      7. What’s in This Book?

    2. Chapter 2 MapReduce

      1. A Weather Dataset
      2. Analyzing the Data with Unix Tools
      3. Analyzing the Data with Hadoop
      4. Scaling Out
      5. Hadoop Streaming

    3. Chapter 3 The Hadoop Distributed Filesystem

      1. The Design of HDFS
      2. HDFS Concepts
      3. The Command-Line Interface
      4. Hadoop Filesystems
      5. The Java Interface
      6. Data Flow
      7. Parallel Copying with distcp

    4. Chapter 4 YARN

      1. Anatomy of a YARN Application Run
      2. YARN Compared to MapReduce 1
      3. Scheduling in YARN
      4. Further Reading

    5. Chapter 5 Hadoop I/O

      1. Data Integrity
      2. Compression
      3. Serialization
      4. File-Based Data Structures

  2. MapReduce

    1. Chapter 1 Developing a MapReduce Application

      1. The Configuration API
      2. Setting Up the Development Environment
      3. Writing a Unit Test with MRUnit
      4. Running Locally on Test Data
      5. Running on a Cluster
      6. Tuning a Job
      7. MapReduce Workflows

    2. Chapter 2 How MapReduce Works

      1. Anatomy of a MapReduce Job Run
      2. Failures
      3. Shuffle and Sort
      4. Task Execution

    3. Chapter 3 MapReduce Types and Formats

      1. MapReduce Types
      2. Input Formats
      3. Output Formats

    4. Chapter 4 MapReduce Features

      1. Counters
      2. Sorting
      3. Joins
      4. Side Data Distribution
      5. MapReduce Library Classes

  3. Hadoop Operations

    1. Chapter 1 Setting Up a Hadoop Cluster

      1. Cluster Specification
      2. Cluster Setup and Installation
      3. Hadoop Configuration
      4. Security
      5. Benchmarking a Hadoop Cluster

    2. Chapter 2 Administering Hadoop

      1. HDFS
      2. Monitoring
      3. Maintenance

  4. Related Projects

    1. Chapter 1 Avro

      1. Avro Data Types and Schemas
      2. In-Memory Serialization and Deserialization
      3. Avro Datafiles
      4. Interoperability
      5. Schema Resolution
      6. Sort Order
      7. Avro MapReduce
      8. Sorting Using Avro MapReduce
      9. Avro in Other Languages

    2. Chapter 2 Parquet

      1. Data Model
      2. Parquet File Format
      3. Parquet Configuration
      4. Writing and Reading Parquet Files
      5. Parquet MapReduce

    3. Chapter 3 Flume

      1. Installing Flume
      2. An Example
      3. Transactions and Reliability
      4. The HDFS Sink
      5. Fan Out
      6. Distribution: Agent Tiers
      7. Sink Groups
      8. Integrating Flume with Applications
      9. Component Catalog
      10. Further Reading

    4. Chapter 4 Sqoop

      1. Getting Sqoop
      2. Sqoop Connectors
      3. A Sample Import
      4. Generated Code
      5. Imports: A Deeper Look
      6. Working with Imported Data
      7. Importing Large Objects
      8. Performing an Export
      9. Exports: A Deeper Look
      10. Further Reading

    5. Chapter 5 Pig

      1. Installing and Running Pig
      2. An Example
      3. Comparison with Databases
      4. Pig Latin
      5. User-Defined Functions
      6. Data Processing Operators
      7. Pig in Practice
      8. Further Reading

    6. Chapter 6 Hive

      1. Installing Hive
      2. An Example
      3. Running Hive
      4. Comparison with Traditional Databases
      5. HiveQL
      6. Tables
      7. Querying Data
      8. User-Defined Functions
      9. Further Reading

    7. Chapter 7 Crunch

      1. An Example
      2. The Core Crunch API
      3. Pipeline Execution
      4. Crunch Libraries
      5. Further Reading

    8. Chapter 8 Spark

      1. Installing Spark
      2. An Example
      3. Resilient Distributed Datasets
      4. Shared Variables
      5. Anatomy of a Spark Job Run
      6. Executors and Cluster Managers
      7. Further Reading

    9. Chapter 9 HBase

      1. HBasics
      2. Concepts
      3. Installation
      4. Clients
      5. Building an Online Query Application
      6. HBase Versus RDBMS
      7. Praxis
      8. Further Reading

    10. Chapter 10 ZooKeeper

      1. Installing and Running ZooKeeper
      2. An Example
      3. The ZooKeeper Service
      4. Building Applications with ZooKeeper
      5. ZooKeeper in Production
      6. Further Reading

    11. Case Studies

      1. Chapter 1 Composable Data at Cerner

        1. From CPUs to Semantic Integration
        2. Enter Apache Crunch
        3. Building a Complete Picture
        4. Integrating Healthcare Data
        5. Composability over Frameworks
        6. Moving Forward

      2. Chapter 2 Biological Data Science: Saving Lives with Software

        1. The Structure of DNA
        2. The Genetic Code: Turning DNA Letters into Proteins
        3. Thinking of DNA as Source Code
        4. The Human Genome Project and Reference Genomes
        5. Sequencing and Aligning DNA
        6. ADAM, A Scalable Genome Analysis Platform
        7. From Personalized Ads to Personalized Medicine
        8. Join In

      3. Chapter 3 Cascading

        1. Fields, Tuples, and Pipes
        2. Operations
        3. Taps, Schemes, and Flows
        4. Cascading in Practice
        5. Flexibility
        6. Hadoop and Cascading at ShareThis
        7. Summary

      4. Appendix Installing Apache Hadoop

        1. Prerequisites
        2. Installation
        3. Configuration

      5. Appendix Cloudera’s Distribution Including Apache Hadoop

      6. Appendix Preparing the NCDC Weather Data

      7. Appendix The Old and New Java MapReduce APIs

  5. Case Studies

    1. Chapter 1 Composable Data at Cerner

      1. From CPUs to Semantic Integration
      2. Enter Apache Crunch
      3. Building a Complete Picture
      4. Integrating Healthcare Data
      5. Composability over Frameworks
      6. Moving Forward

    2. Chapter 2 Biological Data Science: Saving Lives with Software

      1. The Structure of DNA
      2. The Genetic Code: Turning DNA Letters into Proteins
      3. Thinking of DNA as Source Code
      4. The Human Genome Project and Reference Genomes
      5. Sequencing and Aligning DNA
      6. ADAM, A Scalable Genome Analysis Platform
      7. From Personalized Ads to Personalized Medicine
      8. Join In

    3. Chapter 3 Cascading

      1. Fields, Tuples, and Pipes
      2. Operations
      3. Taps, Schemes, and Flows
      4. Cascading in Practice
      5. Flexibility
      6. Hadoop and Cascading at ShareThis
      7. Summary

    4. Appendix Installing Apache Hadoop

      1. Prerequisites
      2. Installation
      3. Configuration

    5. Appendix Cloudera’s Distribution Including Apache Hadoop

    6. Appendix Preparing the NCDC Weather Data

    7. Appendix The Old and New Java MapReduce APIs