You are previewing Data Just Right: Introduction to Large-Scale Data & Analytics.
O'Reilly logo
Data Just Right: Introduction to Large-Scale Data & Analytics

Book Description

Making Big Data Work: Real-World Use Cases and Examples, Practical Code, Detailed Solutions

Large-scale data analysis is now vitally important to virtually every business. Mobile and social technologies are generating massive datasets; distributed cloud computing offers the resources to store and analyze them; and professionals have radically new technologies at their command, including NoSQL databases. Until now, however, most books on “Big Data” have been little more than business polemics or product catalogs. Data Just Right is different: It’s a completely practical and indispensable guide for every Big Data decision-maker, implementer, and strategist.

Michael Manoochehri, a former Google engineer and data hacker, writes for professionals who need practical solutions that can be implemented with limited resources and time. Drawing on his extensive experience, he helps you focus on building applications, rather than infrastructure, because that’s where you can derive the most value.

Manoochehri shows how to address each of today’s key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You’ll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. Throughout, the author demonstrates techniques using many of today’s leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery.

Coverage includes

  • Mastering the four guiding principles of Big Data success—and avoiding common pitfalls

  • Emphasizing collaboration and avoiding problems with siloed data

  • Hosting and sharing multi-terabyte datasets efficiently and economically

  • “Building for infinity” to support rapid growth

  • Developing a NoSQL Web app with Redis to collect crowd-sourced data

  • Running distributed queries over massive datasets with Hadoop, Hive, and Shark

  • Building a data dashboard with Google BigQuery

  • Exploring large datasets with advanced visualization

  • Implementing efficient pipelines for transforming immense amounts of data

  • Automating complex processing with Apache Pig and the Cascading Java library

  • Applying machine learning to classify, recommend, and predict incoming information

  • Using R to perform statistical analysis on massive datasets

  • Building highly efficient analytics workflows with Python and Pandas

  • Establishing sensible purchasing strategies: when to build, buy, or outsource

  • Previewing emerging trends and convergences in scalable data technologies and the evolving role of the Data Scientist 

Table of Contents

  1. Contents
  2. Introduction
    1. Who This Book is For
    2. Why now?
    3. The Internet of... Everything
    4. A Journey toward Ubiquitous Computing
  3. Part I: Directives in the Big Data Era
    1. 1. Four Rules for Data Success
      1. When Data Became a BIG Deal
      2. Data and the Single Server
      3. The Big Data Trade-Off
      4. Anatomy of a Big Data Pipeline
      5. The Ultimate Database
      6. Summary
  4. Part II: Collecting and Sharing a lot of Data
    1. 2. Hosting and Sharing Terabytes of Raw Data
      1. Suffering from Files
      2. Storage: Infrastructure as a Service
      3. Choosing the Right Data Format
      4. Character Encoding
      5. Data in Motion: Data Serialization Formats
      6. Summary
    2. 3. Building a NoSQL-Based Web App to Collect Crowd-Sourced Data
      1. Relational Databases: Command and Control
      2. Relational Databases versus the Internet
      3. Non-relational Database Models
      4. Leaning toward Write Performance: Redis
      5. Sharding across Many Redis Instances
      6. NewSQL: The Return of Codd
      7. Summary
    3. 4. Strategies for Dealing with Data Silos
      1. A Warehouse Full of Jargon
      2. Hadoop: The Elephant in the Warehouse
      3. Data Silos Can Be Good
      4. Convergence: The End of the Data Silo
      5. Summary
  5. Part III: Asking Questions about Your Data
    1. 5. Using Hadoop, Hive and Shark to Ask Questions about Large Datasets
      1. What Is a Data Warehouse?
      2. Apache Hive: Interactive Querying for Hadoop
      3. Shark: Queries at the Speed of RAM
      4. Data Warehousing in the Cloud
      5. Summary
    2. 6. Building a Data Dashboard With Google BigQuery
      1. Analytical Databases
      2. Dremel: Spreading the Wealth
      3. BigQuery: Data Analytics as a Service
      4. Building a Custom Big Data Dashboard
      5. Authorizing Access to the BigQuery API
      6. The Future of Analytical Query Engines
      7. Summary
    3. 7. Visualization Strategies for Exploring Large Datasets
      1. Cautionary Tales: Translating Data into Narrative
      2. Human Scale versus Machine Scale
      3. Building Applications for Data Interactivity
      4. Summary
  6. Part IV: Data Pipelines and Real Time Data
    1. 8. Putting it Together: MapReduce Data Pipelines
      1. What is a Data Pipeline?
      2. Data Pipelines with Hadoop Streaming
      3. A One-Step MapReduce Transformation
      4. Managing Complexity: Python MapReduce Frameworks for Hadoop
      5. Summary
    2. 9. Building Data Transformation Workflows with Pig and Cascading
      1. Large Scale Data Workflows in Practice
      2. It’s Complicated: Multistep MapReduce Transformations
      3. Cascading: Building Robust Data Workflow Applications
      4. When to Choose Pig versus Cascading
      5. Summary
  7. Part V: Machine Learning for Large Datasets
    1. 10. Building a Data Classification System with Mahout
      1. Can Machines Predict the Future?
      2. Challenges of Machine Learning
      3. Apache Mahout: Scaleable Machine Learning
      4. MLBase: Distributed Machine Learning Framework
      5. Summary
  8. Part VI: Statistical Analysis for Massive Datasets
    1. 11. Using R with Large Datasets
      1. Why Statistics Are Sexy
      2. Strategies for Dealing with Large Datasets
      3. Summary
    2. 12. Building Analytics Workflows Using Python and Pandas
      1. The Snakes Are Loose in the Data Zoo
      2. Python Libraries for Data Processing
      3. Building More Complex Workflows
      4. iPython: Completing the Scientific Computing Toolchain
      5. Summary
  9. Part VII: Practical Solutions to Big Data
    1. 13. When to Build, When to Buy, When to Outsource
      1. Overlapping Solutions
      2. Understanding Your Data Problem
      3. A Playbook for the Build Versus Buy Problem
      4. My Own Private Data Center
      5. Understand the Costs of OpenSource
      6. Everything as a Service
      7. Summary
  10. Part VIII: Looking Ahead
    1. 14. The Future: Trends in Data Technology
      1. Hadoop: the Disruptor and the Disrupted
      2. Everything in the Cloud
      3. The Rise and Fall of the Data Scientist
      4. Convergence: The Ultimate Database
      5. Convergence of Cultures
      6. Summary