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Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources

Book Description

Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze, say, web server log files and patterns of user access in real time, as the access is occurring. Gone are the days when you need be caught out by shifting public opinion or sudden changes in customer behavior. Splunk's easy to use engine helps you recognize and react in real time, as events are occurring.

Splunk is a powerful, yet simple analytical tool fast gaining traction in the fields of big data and operational intelligence. Using Splunk, you can monitor data in real time, or mine your data after the fact. Splunk's stunning visualizations aid in locating the needle of value in a haystack of a data. Geolocation support spreads your data across a map, allowing you to drill down to geographic areas of interest. Alerts can run in the background and trigger to warn you of shifts or events as they are taking place.

With Splunk you can immediately recognize and react to changing trends and shifting public opinion as expressed through social media, and to new patterns of eCommerce and customer behavior. The ability to immediately recognize and react to changing trends provides a tremendous advantage in today's fast-paced world of Internet business. Big Data Analytics Using Splunk opens the door to an exciting world of real-time operational intelligence.

  • Built around hands-on projects

  • Shows how to mine social media

  • Opens the door to real-time operational intelligence

What you'll learn

  • Monitor and mine social media for trends affecting your business

  • Know how you are perceived, and when that perception is rising or falling

  • Detect changing customer behavior from mining your operational data

  • Collect and analyze in real time, or from historical files

  • Apply basic analytical metrics to better understand your data

  • Create compelling visualizations and easily communicate your findings

Who this book is for

Big Data Analytics Using Splunk is for those who are interested in exploring the heaps of data they have available, but don't know where to start. It is for the people who have knowledge of the data they want to analyze and are developers or SQL programmers at a level anywhere between beginners and intermediate. Expert developers also benefit from learning how to use such a simple and powerful tool as Splunk.

Table of Contents

  1. Title Page
  2. Dedication
  3. Contents at a Glance
  4. Contents
  5. About the Authors
  6. About the Technical Reviewer
  7. Acknowledgments
  8. CHAPTER 1: Big Data and Splunk
    1. What Is Big Data?
    2. Alternate Data Processing Techniques
    3. What Is Splunk?
    4. About This Book
  9. CHAPTER 2: Getting Data into Splunk
    1. Variety of Data
    2. How Splunk deals with a variety of data
    3. Apps and Add-ons
    4. Forwarders
    5. Summary
  10. CHAPTER 3: Processing and Analyzing the Data
    1. Getting to Know Combined Access Log Data
    2. Searching and Analyzing Indexed Data
    3. Reporting
    4. Sorting
    5. Filtering
    6. Adding and Evaluating Fields
    7. Grouping
    8. Summary
  11. CHAPTER 4: Visualizing the Results
    1. Data Visualization
    2. How Splunk Deals with Visualization
    3. Chart
    4. Timechart
    5. Visualization Using Google Maps App
    6. Globe
    7. Dashboards
    8. Summary
  12. CHAPTER 5: Defining Alerts
    1. What Are Alerts?
    2. How Splunk Provides Alerts
    3. Summary
  13. CHAPTER 6: Web Site Monitoring
    1. Monitoring web sites
    2. IT Operations
    3. Business
    4. Summary
  14. CHAPTER 7: Using Log Files To Create Advanced Analytics
    1. Traditional Analytics
    2. A Paradigm Change
    3. Semantic Logging
    4. Logging Best Practices
    5. Summary
  15. CHAPTER 8: The Airline On-Time Performance Project
    1. Summary
  16. CHAPTER 9: Getting the Flight Data into Splunk
    1. Working with CSV Files
    2. Indexing Data from a Relational Database
    3. Summary
  17. CHAPTER 10: Analyzing Airlines, Airports, Flights, and Delays
    1. Analyzing Airlines
    2. Analyzing Airports
    3. Analyzing Flights
    4. Analyzing Delays
    5. Creating and Using Macros
    6. Report Acceleration
    7. Accelerating Statistics
    8. Summary
  18. CHAPTER 11: Analyzing a Specific Flight Over the Years
    1. Airline Names
    2. United Flight 871
    3. Summary
  19. CHAPTER 12: Analyzing Tweets
    1. Tapping the Sample Stream
    2. Loading the Tweets into Splunk
    3. A Day in Twitter
    4. Real-Time Twitter Trends
    5. Summary
  20. CHAPTER 13: Analyzing Foursquare Check-Ins
    1. The Check-In Format
    2. Time Zone Considerations
    3. Loading the Check-Ins
    4. Analyzing the Check-Ins
    5. Summary
  21. CHAPTER 14: Sentiment Analysis
    1. Opinions, Views, Beliefs, Convictions
    2. Commercial Uses
    3. The Technical Side of Sentiment Analysis
    4. The Sentiment Analysis App
    5. The World Sentiment Indicator Project
    6. Summary
  22. CHAPTER 15: Remote Data Collection
    1. Forwarders
    2. Deployment Server
    3. Deployment Monitor
    4. Summary
  23. CHAPTER 16: Scaling and High Availability
    1. Scaling Splunk
    2. Clustering
    3. Summary
  24. APPENDIX A: The Performance of Splunk
    1. Types of Searches
    2. Indexing Performance
    3. Disk Speed and Searches
    4. Understanding your Splunk Environment
    5. Summary
  25. APPENDIX B: Useful Splunk Apps
    1. Splunk Apps
  26. Index