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Hadoop For Dummies

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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


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Vörumerki: Dummies Series
Vörunúmer: 9781118652206
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Hadoop For Dummies

Vörumerki: Dummies Series
Vörunúmer: 9781118652206
Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup

Veldu vöru

2.190 kr.
Fá vöru senda með tölvupósti
2.190 kr.