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Streaming Analytics with IBM Streams: Analyze More, Act Faster, and Get Continuous Insights

Book Description

Gain a competitive edge with IBM Streams

Turn data-in-motion into solid business opportunities with IBM Streams and let Streaming Analytics with IBM Streams show you how. This comprehensive guide starts out with a brief overview of different technologies used for big data processing and explanations on how data-in-motion can be utilized for business advantages. You will learn how to apply big data analytics and how they benefit from data-in-motion. Discover all about Streams starting with the main components then dive further with Stream instillation, and upgrade and management capabilities including tools used for production. Through a solid understanding of big in motion, detailed illustrations, Endnotes that provide additional learning resources, and end of chapter summaries with helpful insight, data analysists and professionals looking to get more from their data will benefit from expert insight on:

  • Data-in-motion processing and how it can be applied to generate new business opportunities
  • The three approaches to processing data in motion and pros and cons of each
  • The main components of Streams from runtime to installation and administration
  • Multiple purposes of the Text Analytics toolkit
  • The evolving Streams ecosystem
  • A detailed roadmap for programmers to quickly become fluent with Streams

Data-in-motion is rapidly becoming a business tool used to discover more about customers and opportunities, however it is only valuable if have the tools and knowledge to analyze and apply. This is an expert guide to IBM Streams and how you can harness this powerful tool to gain a competitive business edge.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. About the Author
  5. Acknowledgments
  6. Contents
  7. Chapter 1: Big Data: At Rest and in Motion
    1. Where Does Big Data Come From?
    2. New Technologies
    3. What about Relational Databases?
    4. SQL
    5. Big Data Architecture
    6. Data in Motion
    7. Eliminating Noise and Reducing Volume
    8. Real-Time Processing
    9. Summary
    10. Endnotes
  8. Chapter 2: Big Data: In-Motion Use Cases
    1. Processing Principles
      1. Data exhaust
      2. Aggregation
      3. Transformation
      4. Correlation
      5. Continuous analysis
    2. Big Data Use Cases
      1. Big Data exploration
      2. Enhanced 360° view of the customer
      3. Security and intelligence
      4. Operational analysis
      5. Data warehouse augmentation
    3. The Internet of Things
      1. The power of habits
      2. Connected cars
      3. Smarter cities
      4. Other possibilities
    4. Use Cases by Industry
      1. Aerospace and defense
      2. Banking
      3. Oil and gas
      4. Electronics
      5. Energy and utility
      6. Government
      7. Healthcare
      8. Insurance
      9. Telecommunication
    5. Summary
    6. Endnotes
  9. Chapter 3: Program, Framework, or Platform
    1. Build Your Own
    2. Using a Distributed Framework
    3. Using a Streaming Platform
      1. IDE
      2. Toolkits
      3. Programming constructs
      4. Extensibility
      5. Monitoring tools
    4. Summary
  10. Chapter 4: Streams
    1. Streams Background
    2. Streams Runtime
    3. Streams Deployment
    4. The Streams Processing Language
      1. Punctuations
      2. Windowing
      3. Data of all types
      4. Expression language
      5. Annotations
      6. Writing operators
    5. Streams Toolkits
      1. The Standard toolkit
      2. The Database (db) toolkit
      3. The Geospatial toolkit
      4. The HBase and HDFS toolkits
      5. The inet and Messaging toolkits
      6. The R-project toolkit
      7. The Telecommunication Event Data Analytics toolkit
      8. The Text toolkit
      9. The Timeseries toolkit
      10. Other toolkits
    6. Open Source Toolkits
    7. The Streams Development Environment
      1. Streams Studio
    8. Streams Installation
      1. Installation
      2. Upgrade
    9. Streams Administration
      1. Streamtool
      2. Domain manager
      3. Streams Console
      4. ReST API
    10. Summary
    11. Endnotes
  11. Chapter 5: Data Science, Statistics, and Streams
    1. Data Science is No Cure-all
    2. Some Data Science Terms
      1. Population and sample
      2. Distribution and its characteristics
      3. Features, derived predictors, and scaling
      4. Classification and regression
      5. Models, machine learning, and algorithms
    3. Data Science Methodology
    4. Data Preparation and Representation
    5. Geospatial Toolkit
      1. Functions
    6. Timeseries Toolkit
    7. Mining Toolkit
    8. SPSS Toolkit
    9. R-project toolkit
    10. SparkMLlib toolkit
    11. Extensible Framework
    12. Summary
  12. Chapter 6: Text Analytics
    1. What is Text Analytics?
      1. Word count revisited
      2. Keywords selection
      3. Extracting Information
    2. Streaming Text Analytics Use-Cases
    3. The AQL Language
      1. Writing extractors
    4. Text Analytics Tooling
    5. Summary
    6. Endnotes
  13. Chapter 7: The Streams Ecosystem
    1. Streams Provided Adapters
      1. The Standard Toolkit adapters
      2. The Big Data Toolkit adapters
      3. The Database Toolkit adapters
      4. The Financial Services Toolkit adapters
      5. The DataStage Integration toolkit
      6. The Hbase toolkit
      7. The Internet Toolkit adapter
      8. The Messaging Toolkit adapters
      9. The Mining toolkit
      10. The R-Project toolkit
      11. The ReST API
    2. Open-Source Toolkits
    3. IBM Products Interfaces
      1. SPSS
      2. IBM Operational Decision Manager (ODM)
      3. MessageSight
    4. Partners and Third-party Products
    5. Streams Extensibility
    6. Summary
    7. Endnotes
  14. Chapter 8: Getting Started
    1. How to Get Streams
    2. Introductory Hands-on Lab
    3. Moving On into More In-depth Learning
      1. The SPL lab
      2. Other labs
      3. Additional topics
    4. Streamsdev
      1. Streamsdev details
    5. Navigating the Streams Documentation
      1. Text analytics
    6. Other Material
      1. The Nifty Fifty
      2. DeveloperWorks
      3. Documentation Sample Code
    7. Summary
    8. Endnotes
  15. Appendix A: Resources and References
    1. Documentation
    2. Wikis
    3. Open-Source Toolkits
    4. Example Code
    5. Streams-related Articles
    6. Internet of Things articles
    7. Smarter Planet