Gain insight into how to govern and consume IBM's unique in-motion and at-rest Big Data analytic capabilitiesA. R. Ammons once said, "A word too much repeated falls out of being", and although the term Big Data sometimes seems to be "too much repeated", it's not about to fall "out of being". That said, it is subject to a lot of hype. The term Big Data is a bit of a misnomer. Truth be told, we're not even big fans of the term--despite the fact that it is so prominently displayed on the cover of this book--because it implies that other data is somehow small (it might be) or that this particular type of data is large in size (it can be, but doesn’t have to be).
This is Big Data in a nutshell: It is the ability to retain, process, and understand data like never before. It can mean more data than what you are using today; but it can also mean different kinds of data, a venture into the unstructured world where most of today's data resides. The Big Data opportunity. It's a shift, rift, lift, or cliff for your business--this book is going to help you experience the shift and lift, while those that don't work to get beyond the hype end up in a rift or cliff.
In this book you will learn how cognitive computing systems, like IBM Watson, fit into the Big Data world. You'll learn how Big Data needs a "ground-to-cloud" architecture, what a Data Refinery looks like, and theimportance of a next generation data platform. Gain an understanding of the concepts of data-in-motion, data-at-rest (technologies like Hadoop play here, as well as others), the role that NoSQL and polyglot play in a leading edge analytics architecture, and more. Get details about the Big Data platform manifesto and why it is a must for any Big Data project. Capturing, storing, refining, transforming, governing, securing, and analyzing data, traditionally or as a service, are important topics alsocovered in this book.
Table of Contents:
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
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