Data Just Right

Introduction to Large-Scale Data & Analytics
 
 
Addison Wesley (Verlag)
  • 1. Auflage
  • |
  • erschienen am 30. November 2013
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-0-13-335907-7 (ISBN)
 

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
1. Auflage
  • Englisch
  • Boston
  • |
  • USA
Pearson Education (US)
  • Für höhere Schule und Studium
  • 7,79 MB
978-0-13-335907-7 (9780133359077)
Michael Manoochehri is an entrepreneur, writer, and optimist. With many years of experience working with enterprise, research, and non-profit organizations, his goal is to help make scalable data analytics more affordable and accessible. Michael has been a member of Google's Cloud Platform developer relations team, focusing on cloud computing and data developer products such as Google BigQuery. In addition, Michael has written for the tech blog ProgrammableWeb.com, has spent time in rural Uganda researching mobile phone use, and holds a master's degree in information management and systems from UC Berkeley's School of Information.
  • Chapter 1: Four Rules for Data Success
  • Chapter 2: Hosting and Sharing Terabytes of Raw Data
  • Chapter 3: Building a NoSQL-Based Web App to Collect Crowd-Sourced Data
  • Chapter 4: Strategies for Dealing with Data Silos
  • Chapter 5: Using Hadoop, Hive, and Shark to Ask Questions about Large Datasets
  • Chapter 6: Building a Data Dashboard with Google BigQuery
  • Chapter 7: Visualization Strategies for Exploring Large Datasets
  • Chapter 8: Putting It Together: MapReduce Data Pipelines
  • Chapter 9: Building Data Transformation Workflows with Pig and Cascading
  • Chapter 10: Building a Data Classification System with Mahout
  • Chapter 11: Using R with Large Datasets
  • Chapter 12: Building Analytics Workflows Using Python and Pandas
  • Chapter 13: When to Build, When to Buy, When to Outsource
  • Chapter 14: The Future: Trends in Data Technology

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