You are previewing Planning for Big Data.

Planning for Big Data

Cover of Planning for Big Data by Edd Dumbill Published by O'Reilly Media, Inc.
  1. Planning for Big Data
  2. Introduction
  3. 1. The Feedback Economy
    1. Data-Obese, Digital-Fast
    2. The Big Data Supply Chain
      1. Data collection
      2. Ingesting and cleaning
      3. Hardware
      4. Platforms
      5. Machine learning
      6. Human exploration
      7. Storage
      8. Sharing and acting
      9. Measuring and collecting feedback
    3. Replacing Everything with Data
    4. A Feedback Economy
  4. 2. What Is Big Data?
    1. What Does Big Data Look Like?
      1. Volume
      2. Velocity
      3. Variety
    2. In Practice
      1. Cloud or in-house?
      2. Big data is big
      3. Big data is messy
      4. Culture
      5. Know where you want to go
  5. 3. Apache Hadoop
    1. The Core of Hadoop: MapReduce
    2. Hadoop’s Lower Levels: HDFS and MapReduce
    3. Improving Programmability: Pig and Hive
    4. Improving Data Access: HBase, Sqoop, and Flume
      1. Getting data in and out
    5. Coordination and Workflow: Zookeeper and Oozie
    6. Management and Deployment: Ambari and Whirr
    7. Machine Learning: Mahout
    8. Using Hadoop
  6. 4. Big Data Market Survey
    1. Just Hadoop?
    2. Integrated Hadoop Systems
      1. EMC Greenplum
      2. IBM
      3. Microsoft
      4. Oracle
      5. Availability
    3. Analytical Databases with Hadoop Connectivity
      1. Quick facts
    4. Hadoop-Centered Companies
      1. Cloudera
      2. Hortonworks
      3. An overview of Hadoop distributions (part 1)
      4. An overview of Hadoop distributions (part 2)
    5. Notes
  7. 5. Microsoft’s Plan for Big Data
    1. Microsoft’s Hadoop Distribution
    2. Developers, Developers, Developers
    3. Streaming Data and NoSQL
    4. Toward an Integrated Environment
    5. The Data Marketplace
    6. Summary
  8. 6. Big Data in the Cloud
    1. IaaS and Private Clouds
    2. Platform solutions
      1. Amazon Web Services
      2. Google
      3. Microsoft
    3. Big data cloud platforms compared
    4. Conclusion
    5. Notes
  9. 7. Data Marketplaces
    1. What Do Marketplaces Do?
    2. Infochimps
    3. Factual
    4. Windows Azure Data Marketplace
    5. DataMarket
    6. Data Markets Compared
    7. Other Data Suppliers
  10. 8. The NoSQL Movement
    1. Size, Response, Availability
    2. Changing Data and Cheap Lunches
    3. The Sacred Cows
    4. Other features
    5. In the End
  11. 9. Why Visualization Matters
    1. A Picture Is Worth 1000 Rows
    2. Types of Visualization
      1. Explaining and exploring
    3. Your Customers Make Decisions, Too
    4. Do Yourself a Favor and Hire a Designer
  12. 10. The Future of Big Data
    1. More Powerful and Expressive Tools for Analysis
    2. Streaming Data Processing
    3. Rise of Data Marketplaces
    4. Development of Data Science Workflows and Tools
    5. Increased Understanding of and Demand for Visualization
  13. About the Author
  14. Copyright
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Chapter 9. Why Visualization Matters

By Julie Steele

A Picture Is Worth 1000 Rows

Let’s say you need to understand thousands or even millions of rows of data, and you have a short time to do it in. The data may come from your team, in which case perhaps you’re already familiar with what it’s measuring and what the results are likely to be. Or it may come from another team, or maybe several teams at once, and be completely unfamiliar. Either way, the reason you’re looking at it is that you have a decision to make, and you want to be informed by the data before making it. Something probably hangs in the balance: a customer, a product, or a profit.

How are you going to make sense of all that information efficiently so you can make a good decision? Data visualization is an important answer to that question.

However, not all visualizations are actually that helpful. You may be all too familiar with lifeless bar graphs, or line graphs made with software defaults and couched in a slideshow presentation or lengthy document. They can be at best confusing, and at worst misleading. But the good ones are an absolute revelation.

The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Understanding those relationships—and so being able to observe them—is key to good decision-making. The Periodic Table is a classic testament to the potential of visualization to reveal hidden relationships in even small data sets. One ...

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