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• Introduction
• About this Book
• Foolish Assumptions
• How This Book Is Organized
• Part I: Getting Started With Hadoop
• Part II: How Hadoop Works
• Part III: Hadoop and Structured Data
• Part IV: Administering and Configuring Hadoop
• Part V: The Part Of Tens: Getting More Out of Your Hadoop Cluster
• Icons Used in This Book
• Beyond the Book
• Where to Go from Here
• Part I: Getting Started with Hadoop
• Chapter 1: Introducing Hadoop and Seeing What It’s Good For
• Big Data and the Need for Hadoop
• Exploding data volumes
• Varying data structures
• A playground for data scientists
• The Origin and Design of Hadoop
• Distributed processing with MapReduce
• Apache Hadoop ecosystem
• Examining the Various Hadoop Offerings
• Comparing distributions
• Working with in-database MapReduce
• Looking at the Hadoop toolbox
• Chapter 2: Common Use Cases for Big Data in Hadoop
• The Keys to Successfully Adopting Hadoop (Or, “Please, Can We Keep Him?”)
• Log Data Analysis
• Data Warehouse Modernization
• Fraud Detection
• Risk Modeling
• Social Sentiment Analysis
• Image Classification
• Graph Analysis
• To Infinity and Beyond
• Chapter 3: Setting Up Your Hadoop Environment
• Choosing a Hadoop Distribution
• Choosing a Hadoop Cluster Architecture
• Pseudo-distributed mode (single node)
• Fully distributed mode (a cluster of nodes)
• The Hadoop For Dummies Environment
• The Hadoop For Dummies distribution: Apache Bigtop
• Setting up the Hadoop For Dummies environment
• The Hadoop For Dummies Sample Data Set: Airline on-time performance
• Your First Hadoop Program: Hello Hadoop!
• Part II: How Hadoop Works
• Chapter 4: Storing Data in Hadoop: The Hadoop Distributed File System
• Data Storage in HDFS
• Taking a closer look at data blocks
• Replicating data blocks
• Slave node and disk failures
• Sketching Out the HDFS Architecture
• Looking at slave nodes
• Keeping track of data blocks with NameNode
• Checkpointing updates
• HDFS Federation
• HDFS High Availability
• Chapter 5: Reading and Writing Data
• Compressing Data
• Managing Files with the Hadoop File System Commands
• Ingesting Log Data with Flume
• Chapter 6: MapReduce Programming
• Thinking in Parallel
• Seeing the Importance of MapReduce
• Doing Things in Parallel: Breaking Big Problems into Many Bite-Size Pieces
• Looking at MapReduce application flow
• Understanding input splits
• Seeing how key/value pairs fit into the MapReduce application flow
• Writing MapReduce Applications
• Getting Your Feet Wet: Writing a Simple MapReduce Application
• The FlightsByCarrier driver application
• The FlightsByCarrier mapper
• The FlightsByCarrier reducer
• Running the FlightsByCarrier application
• Chapter 7: Frameworks for Processing Data in Hadoop: YARN and MapReduce
• Running Applications Before Hadoop 2
• Tracking JobTracker
• Tracking TaskTracker
• Launching a MapReduce application
• Seeing a World beyond MapReduce
• Scouting out the YARN architecture
• Launching a YARN-based application
• Real-Time and Streaming Applications
• Chapter 8: Pig: Hadoop Programming Made Easier
• Admiring the Pig Architecture
• Going with the Pig Latin Application Flow
• Working through the ABCs of Pig Latin
• Uncovering Pig Latin structures
• Looking at Pig data types and syntax
• Evaluating Local and Distributed Modes of Running Pig scripts
• Checking Out the Pig Script Interfaces
• Scripting with Pig Latin
• Chapter 9: Statistical Analysis in Hadoop
• Pumping Up Your Statistical Analysis
• The limitations of sampling
• Factors that increase the scale of statistical analysis
• Running statistical models in MapReduce
• Machine Learning with Mahout
• Collaborative filtering
• Clustering
• Classifications
• R on Hadoop
• The R language
• Hadoop Integration with R
• Chapter 10: Developing and Scheduling Application Workflows with Oozie
• Getting Oozie in Place
• Developing and Running an Oozie Workflow
• Writing Oozie workflow definitions
• Configuring Oozie workflows
• Running Oozie workflows
• Scheduling and Coordinating Oozie Workflows
• Time-based scheduling for Oozie coordinator jobs
• Time and data availability-based scheduling for Oozie coordinator jobs
• Running Oozie coordinator jobs
• Part III: Hadoop and Structured Data
• Chapter 11: Hadoop and the Data Warehouse: Friends or Foes?
• Comparing and Contrasting Hadoop with Relational Databases
• NoSQL data stores
• ACID versus BASE data stores
• Structured data storage and processing in Hadoop
• Modernizing the Warehouse with Hadoop
• The landing zone
• A queryable archive of cold warehouse data
• Hadoop as a data preprocessing engine
• Data discovery and sandboxes
• Chapter 12: Extremely Big Tables: Storing Data in HBase
• Say Hello to HBase
• Sparse
• It’s distributed and persistent
• It has a multidimensional sorted map
• Understanding the HBase Data Model
• Understanding the HBase Architecture
• RegionServers
• MasterServer
• Zookeeper and HBase reliability
• Taking HBase for a Test Run
• Creating a table
• Working with Zookeeper
• Getting Things Done with HBase
• Working with an HBase Java API client example
• HBase and the RDBMS world
• Knowing when HBase makes sense for you?
• ACID Properties in HBase
• Transitioning from an RDBMS model to HBase
• Deploying and Tuning HBase
• Hardware requirements
• Deployment Considerations
• Tuning prerequisites
• Understanding your data access patterns
• Pre-Splitting your regions
• The importance of row key design
• Tuning major compactions
• Chapter 13: Applying Structure to Hadoop Data with Hive
• Saying Hello to Hive
• Seeing How the Hive is Put Together
• Getting Started with Apache Hive
• Examining the Hive Clients
• The Hive CLI client
• The web browser as Hive client
• SQuirreL as Hive client with the JDBC Driver
• Working with Hive Data Types
• Creating and Managing Databases and Tables
• Managing Hive databases
• Creating and managing tables with Hive
• Seeing How the Hive Data Manipulation Language Works
• LOAD DATA examples
• INSERT examples
• Create Table As Select (CTAS) examples
• Querying and Analyzing Data
• Joining tables with Hive
• Improving your Hive queries with indexes
• Windowing in HiveQL
• Other key HiveQL features
• Chapter 14: Integrating Hadoop with Relational Databases Using Sqoop
• The Principles of Sqoop Design
• Scooping Up Data with Sqoop
• Connectors and Drivers
• Importing Data with Sqoop
• Importing data into HDFS
• Importing data into Hive
• Importing data into HBase
• Importing incrementally
• Benefiting from additional Sqoop import features
• Sending Data Elsewhere with Sqoop
• Exporting data from HDFS
• Sqoop exports using the Insert approach
• Sqoop exports using the Update and Update Insert approach
• Sqoop exports using call stored procedures
• Sqoop exports and transactions
• Looking at Your Sqoop Input and Output Formatting Options
• Getting down to brass tacks: An example of output line-formatting and input-parsing
• Sqoop 2.0 Preview
• Chapter 15: The Holy Grail: Native SQL Access to Hadoop Data
• SQL’s Importance for Hadoop
• Looking at What SQL Access Actually Means
• SQL Access and Apache Hive
• Solutions Inspired by Google Dremel
• Apache Drill
• Cloudera Impala
• IBM Big SQL
• Pivotal HAWQ
• Hadapt
• The SQL Access Big Picture
• Part IV: Administering and Configuring Hadoop
• Chapter 16: Deploying Hadoop
• Working with Hadoop Cluster Components
• Rack considerations
• Master nodes
• Slave nodes
• Edge nodes
• Networking
• Hadoop Cluster Configurations
• Small
• Medium
• Large
• Alternate Deployment Form Factors
• Virtualized servers
• Cloud deployments
• Sizing Your Hadoop Cluster
• Chapter 17: Administering Your Hadoop Cluster
• Achieving Balance: A Big Factor in Cluster Health
• Mastering the Hadoop Administration Commands
• Understanding Factors for Performance
• Hardware
• MapReduce
• Benchmarking
• Tolerating Faults and Data Reliability
• Putting Apache Hadoop’s Capacity Scheduler to Good Use
• Setting Security: The Kerberos Protocol
• Expanding Your Toolset Options
• Hue
• Ambari
• Hadoop User Experience (Hue)
• The Hadoop shell
• Basic Hadoop Configuration Details
• Part V: The Part of Tens
• Chapter 18: Ten Hadoop Resources Worthy of a Bookmark
• Central Nervous System: Apache.org
• Tweet This
• Hortonworks University
• Cloudera University
• BigDataUniversity.com
• planet Big Data Blog Aggregator
• Quora’s Apache Hadoop Forum
• The IBM Big Data Hub
• Conferences Not to Be Missed
• The Google Papers That Started It All
• The Bonus Resource: What Did We Ever Do B.G.?
• Chapter 19: Ten Reasons to Adopt Hadoop
• Hadoop Is Relatively Inexpensive
• Hadoop Has an Active Open Source Community
• Hadoop Is Being Widely Adopted in Every Industry
• Hadoop Can Easily Scale Out As Your Data Grows
• Traditional Tools Are Integrating with Hadoop
• Hadoop Can Store Data in Any Format
• Hadoop Is Designed to Run Complex Analytics
• Hadoop Can Process a Full Data Set (As Opposed to Sampling)
• Hardware Is Being Optimized for Hadoop
• Hadoop Can Increasingly Handle Flexible Workloads (No Longer Just Batch)
• About the Authors
• Cheat Sheet
• More Dummies Products