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Big Data Now: 2014 Edition

Book Description

In the four years that O’Reilly has produced its annual Big Data Now report, the data field has grown from infancy into young adulthood. Data is now a leader in some fields and a driver of innovation in others, and companies that use data and analytics to drive decision-making are outperforming their peers.

And while access to big data tools and techniques once required significant expertise, today many tools have improved and communities have formed to share best practices. Companies have also started to emphasize the importance of processes, culture, and people.

The topics in this 2014 edition of Big Data Now represent the major forces currently shaping the data world:

  • Cognitive augmentation: predictive APIs, graph analytics, and Network Science dashboards
  • Intelligence matters: defining AI, modeling intelligence, deep learning, and "summoning the demon"
  • Cheap sensors, fast networks, and distributed computing: stream processing, hardware data flows, and computing at the edge
  • Data (science) pipelines: broadening the coverage of analytic pipelines with specialized tools
  • Evolving marketplace of big data components: SSDs, Hadoop 2, Spark; and why datacenters need operating systems
  • Design and social science: human-centered design, wearables and real-time communications, and wearable etiquette
  • Building a data culture: moving from prediction to real-time adaptation; and why you need to become a data skeptic
  • Perils of big data: data redlining, intrusive data analysis, and the state of big data ethics

Table of Contents

  1. Introduction: Big Data’s Big Ideas
  2. 1. Cognitive Augmentation
    1. Challenges Facing Predictive APIs
      1. Data Gravity
      2. Workflow
      3. Crossing the Development/Production Divide
      4. Users and Skill Sets
      5. Horizontal versus Vertical
    2. There Are Many Use Cases for Graph Databases and Analytics
    3. Network Science Dashboards
  3. 2. Intelligence Matters
    1. AI’s Dueling Definitions
    2. In Search of a Model for Modeling Intelligence
    3. Untapped Opportunities in AI
    4. What is Deep Learning, and Why Should You Care?
    5. Artificial Intelligence: Summoning the Demon
  4. 3. The Convergence of Cheap Sensors, Fast Networks, and Distributed Computing
    1. Expanding Options for Mining Streaming Data
      1. Leveraging and Deploying Storm
      2. Focus on Analytics Instead of Infrastructure
      3. Machine-Learning
    2. Embracing Hardware Data
    3. Extracting Value from the IoT
    4. Fast Data Calls for New Ways to Manage Its Flow
    5. Clouds, Edges, Fog, and the Pendulum of Distributed Computing
  5. 4. Data (Science) Pipelines
    1. Verticalized Big Data Solutions
      1. Better Tools Can’t Overcome Poor Analysis
    2. Scaling Up Data Frames
      1. Spark
      2. R
      3. Python
      4. Badger
    3. Streamlining Feature Engineering
      1. Feature Engineering or the Creation of New Features
      2. Feature Selection Techniques
      3. Expect More Tools to Streamline Feature Discovery
    4. Big Data Solutions Through the Combination of Tools
  6. 5. The Evolving, Maturing Marketplace of Big Data Components
    1. How Flash changes the design of database storage engines
      1. Key Characteristics of Flash that Influence Databases
      2. The Order of the Day
      3. Write Right
      4. Keep ‘Em Coming
    2. Introduction to Hadoop 2.0
    3. A Growing Number of Applications Are Being Built with Spark
    4. Why the Data Center Needs an Operating System
      1. Machines are the Wrong Abstraction
      2. If My Laptop Were a Data Center
      3. It’s Time for the Data Center OS
      4. An API for the Data Center
      5. Example Primitives
      6. A New Way to Deploy Applications
      7. The “Cloud” is Not an Operating System
      8. Apache Mesos: The Distributed Systems Kernel
  7. 6. Design and Social Science
    1. How Might We...
      1. Technique #1: The Welcoming Culture
      2. Technique #2: The “Departure Point”
      3. Technique #3: The “Dream View”
      4. Technique #4: Gold Stars and Other Props
      5. Technique #5: On Being a Mediator
    2. Wearables and the Immediacy of Communication
    3. Wearable Intelligence
  8. 7. Building a Data Culture
    1. Understanding the Now: The Role of Data in Adaptive Organizations
      1. Slow and Unaware
      2. Embracing Uncertainty
      3. From Prediction to Adaptation
      4. A Holistic View
    2. The Backlash against Big Data, Continued
  9. 8. The Perils of Big Data
    1. One Man Willingly Gave Google His Data. See What Happened Next.
    2. The Creep Factor
    3. Big Data and Privacy: An Uneasy Face-Off for Government to Face
      1. A Narrow Horizon for Privacy
      2. Questions the Government is Asking Itself, and Us
      3. Incentives and Temptations
      4. Having Our Cake
      5. Privacy and Dignity
    4. What’s Up with Big Data Ethics?