Produktbild: Big Data Beyond the Hype: A Guide to Conversations for Today's Data Center

Big Data Beyond the Hype: A Guide to Conversations for Today's Data Center

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.11.2014

Abbildungen

Illustrationen, nicht spezifiziert

Verlag

Mcgraw Hill Higher Education

Seitenzahl

394

Maße (L/B/H)

22,9/15,2/2,1 cm

Gewicht

526 g

Sprache

Englisch

ISBN

978-0-07-184465-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.11.2014

Abbildungen

Illustrationen, nicht spezifiziert

Verlag

Mcgraw Hill Higher Education

Seitenzahl

394

Maße (L/B/H)

22,9/15,2/2,1 cm

Gewicht

526 g

Sprache

Englisch

ISBN

978-0-07-184465-9

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Big Data Beyond the Hype: A Guide to Conversations for Today's Data Center
  • Introduction
    Part I Opening Conversations About Big Data
    1 Getting Hype out of the Way: Big Data and Beyond
    There's Gold in "Them There" Hills!
    Why Is Big Data Important?
    Brought to You by the Letter V: How We Define Big Data
    Cognitive Computing
    Why Does the Big Data World Need Cognitive Computing?
    A Big Data and Analytics Platform Manifesto
    1. Discover, Explore, and Navigate Big Data Sources
    2. Land, Manage, and Store Huge Volumes of Any Data
    3. Structured and Controlled Data
    4. Manage and Analyze Unstructured Data
    5. Analyze Data in Real Time
    6. A Rich Library of Analytical Functions and Tools
    7. Integrate and Govern All Data Sources
    Cognitive Computing Systems
    Of Cloud and Manifestos...
    Wrapping It Up
    2 To SQL or Not to SQL: That's Not the Question, It's the Era of Polyglot Persistence
    Core Value Systems: What Makes a NoSQL Practitioner Tick
    What Is NoSQL?
    Is Hadoop a NoSQL Database?
    Different Strokes for Different Folks: The NoSQL Classification System
    Give Me a Key, I'll Give You a Value: The Key/Value Store
    The Grand-Daddy of Them All: The Document Store
    Column Family, Columnar Store, or BigTable Derivatives: What Do We Call You?
    Don't Underestimate the Underdog: The Graph Store
    From ACID to CAP
    CAP Theorem and a Meatloaf Song: "Two Out of Three Ain't Bad"
    Let Me Get This Straight: There Is SQL, NoSQL, and Now NewSQL?
    Wrapping It Up
    3 Composing Cloud Applications: Why We Love the Bluemix and the IBM Cloud
    At Your Service: Explaining Cloud Provisioning Models
    Setting a Foundation for the Cloud: Infrastructure as a Service
    IaaS for Tomorrow...Available Today: IBM SoftLayer Powers the IBM Cloud
    Noisy Neighbors Can Be Bad Neighbors: The Multitenant Cloud
    Building the Developer's Sandbox with Platform as a Service
    If You Have Only a Couple of Minutes: PaaS and IBM Bluemix in a Nutshell
    Digging Deeper into PaaS
    Being Social on the Cloud: How Bluemix Integrates Platforms and Architectures
    Understanding the Hybrid Cloud: Playing Frankenstein Without the Horror
    Tried and Tested: How Deployable Patterns Simplify PaaS
    Composing the Fabric of Cloud Services: IBM Bluemix
    Parting Words on Platform as a Service
    Consuming Functionality Without the Stress: Software as a Service
    The Cloud Bazaar: SaaS and the API Economy
    Demolishing the Barrier to Entry for Cloud-Ready Analytics: IBM's dashDB
    Build More, Grow More, Know More: dashDB's Cloud SaaS
    Refinery as a Service
    Wrapping It Up
    4 The Data Zones Model: A New Approach to Managing Data
    Challenges with the Traditional Approach
    Agility
    Cost
    Depth of Insight
    Next-Generation Information Management Architectures
    Prepare for Touchdown: The Landing Zone
    Into the Unknown: The Exploration Zone
    Into the Deep: The Deep Analytic Zone
    Curtain Call: The New Staging Zone
    You Have Questions? We Have Answers! The Queryable Archive Zone
    In Big Data We Trust: The Trusted Data Zone
    A Zone for Business Reporting
    From Forecast to Nowcast: The Real-Time Processing and Analytics Zone
    Ladies and Gentlemen, Presenting... "The Data Zones Model"
    Part II Watson Foundations
    5 Starting Out with a Solid Base: A Tour of Watson Foundations
    Overview of Watson Foundations
    A Continuum of Analytics Capabilities: Foundations for Watson
    6 Landing Your Data in Style with Blue Suit Hadoop: InfoSphere BigInsights
    Where Do Elephants Come From: What Is Hadoop?
    A Brief History of Hadoop
    Components of Hadoop and Related Projects
    Open Source...and Proud of It
    Making Analytics on Hadoop Easy
    The Real Deal for SQL on Hadoop: Big SQL
    Machine Learning for the Masses: Big R and SystemML
    The Advanced Text Analytics Toolkit
    Data Discovery and Visualization: BigSheets
    Spatiotemporal Analytics
    Finding Needles in Haystacks of Needles: Indexing and Search in BigInsights
    Cradle-to-Grave Application Development Support
    The BigInsights Integrated Development Environment
    The BigInsights Application Lifecycle
    An App Store for Hadoop: Easy Deployment and Execution of Custom Applications
    Keeping the Sandbox Tidy: Sharing and Managing Hadoop
    The BigInsights Web Console
    Monitoring the Aspects of Your Cluster
    Securing the BigInsights for Hadoop Cluster
    Adaptive MapReduce
    A Flexible File System for Hadoop: GPFS-FPO
    Playing Nice: Integration with Other Data Center Systems
    IBM InfoSphere System z Connector for Hadoop
    IBM PureData System for Analytics
    InfoSphere Streams for Data in Motion
    InfoSphere Information Server for Data Integration
    Matching at Scale with Big Match
    Securing Hadoop with Guardium and Optim
    Broad Integration Support
    Deployment Flexibility
    BigInsights Editions: Free, Low-Cost, and Premium Offerings
    A Low-Cost Way to Get Started: Running BigInsights on the Cloud
    Higher-Class Hardware: Power and System z Support
    Get Started Quickly!
    Wrapping It Up
    7 "In the Moment" Analytics: InfoSphere Streams
    Introducing Streaming Data Analysis
    How InfoSphere Streams Works
    A Simple Streams Application
    Recommended Uses for Streams
    How Is Streams Different from CEP Systems?
    Stream Processing Modes: Preserve Currency or Preserve Each Record
    High Availability
    Dynamically Distributed Processing
    InfoSphere Streams Platform Components
    The Streams Console
    An Integrated Development Environment for Streams: Streams Studio
    The Streams Processing Language
    Source and Sink Adapters
    Analytical Operators
    Streams Toolkits
    Solution Accelerators
    Use Cases
    Get Started Quickly!
    Wrapping It Up
    8 700 Million Times Faster Than the Blink of an Eye: BLU Acceleration
    What Is BLU Acceleration?
    What Does a Next Generation Database Service for Analytics Look Like?
    Seamlessly Integrated
    Hardware Optimized
    Convince Me to Take BLU Acceleration for a Test Drive
    Pedal to the Floor: How Fast Is BLU Acceleration?
    From Minimized to Minuscule: BLU Acceleration Compression Ratios
    Where Will I Use BLU Acceleration?
    How BLU Acceleration Came to Be: Seven Big Ideas
    Big Idea #1: KISS It!
    Big Idea #2: Actionable Compression and Computer-Friendly Encoding
    Big Idea #3: Multiplying the Power of the CPU
    Big Idea #4: Parallel Vector Processing
    Big Idea #5: Get Organized...by Column
    Big Idea #6: Dynamic In-Memory Processing
    Big Idea #7: Data Skipping
    How Seven Big Ideas Optimize the Hardware Stack
    The Sum of All Big Ideas: BLU Acceleration in Action
    DB2 with BLU Acceleration Shadow Tables: When OLTP + OLAP = 1 DB
    What Lurks in These Shadows Isn't Anything to Be Scared of: Operational Reporting
    Wrapping It Up
    9 An Expert Integrated System for Deep Analytics
    Before We Begin: Bursting into the Cloud
    Starting on the Whiteboard: Netezza's Design Principles
    Appliance Simplicity: Minimize the Human Effort
    Process Analytics Closer to the Data Store
    Balanced + MPP = Linear Scalability
    Modular Design: Support Flexible Configurations and Extreme Scalability
    What's in the Box? The Netezza Appliance Architecture Overview
    A Look Inside a Netezza Box
    How a Query Runs in Netezza
    How Netezza Is a Platform for Analytics
    Wrapping It Up
    10 Build More, Grow More, Sleep More: IBM Cloudant
    Cloudant: "White Glove" Database as a Service
    Where Did Cloudant Roll in From?
    Cloudant or Hadoop?
    Being Flexible: Schemas with JSON
    Cloudant Clustering: Scaling for the Cloud
    Avoiding Mongo-Size Outages: Sleep Soundly with Cloudant Replication
    Cloudant Sync Brings Data to a Mobile World
    Make Data, Not War: Cloudant Versioning and Conflict Resolution
    Unlocking GIS Data with Cloudant Geospatial
    Cloudant Local
    Here on In: For Techies...
    For Techies: Leveraging the Cloudant Primary Index
    Exploring Data with Cloudant's Secondary Index "Views"
    Performing Ad Hoc Queries with the Cloudant Search Index
    Parameters That Govern a Logical Cloudant Database
    Remember! Cloudant Is DBaaS
    Wrapping It Up
    Part III Calming the Waters: Big Data Governance
    11 Guiding Principles for Data Governance
    The IBM Data Governance Council Maturity Model
    Wrapping It Up
    12 Security Is NOT an Afterthought
    Security Big Data: How It's Different
    Securing Big Data in Hadoop
    Culture, Definition, Charter, Foundation, and Data Governance
    What Is Sensitive Data?
    The Masquerade Gala: Masking Sensitive Data
    Don't Break the DAM: Monitoring and Controlling Access to Data
    Protecting Data at Rest
    Wrapping It Up
    13 Big Data Lifecycle Management
    A Foundation for Data Governance: The Information Governance Catalog
    Data on Demand: Data Click
    Data Integration
    Data Quality
    Veracity as a Service: IBM DataWorks
    Managing Your Test Data: Optim Test Data Management
    A Retirement Home for Your Data: Optim Data Archive
    Wrapping It Up
    14 Matching at Scale: Big Match
    What Is Matching Anyway?
    A Teaser: Where Are You Going to Use Big Match?
    Matching on Hadoop
    Matching Approaches
    Big Match Architecture
    Big Match Algorithm Configuration Files
    Big Match Applications
    HBase Tables
    Probabilistic Matching Engine
    How It Works
    Extract
    Search
    Applications for Big Match
    Enabling the Landing Zone
    Enhanced 360-Degree View of Your Customers
    More Reliable Data Exploration
    Large-Scale Searches for Matching Records
    Wrapping It Up