Data Just Right: Introduction to Large-Scale Data & Analytics

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

Part I: Directives in the Big Data Era
Chapter 1. Four Rules for Data Success

Part II: Collecting and Sharing a Lot of Data
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

Part III: Asking Questions about Your Data
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

Part IV: Building Data Pipelines
Chapter 8. Putting It Together: MapReduce Data Pipelines
Chapter 9. Building Data Transformation Workflows with Pig and Cascading

Part V: Machine Learning for Large Datasets
Chapter 10. Building a Data Classification System with Mahout

Part VI: Statistical Analysis for Massive Datasets
Chapter 11. Using R with Large Datasets
Chapter 12. Building Analytics Workflows Using Python and Pandas

Part VII: Looking Ahead
Chapter 13. When to Build, When to Buy, When to Outsource
Chapter 14. The Future. Trends in Data Technology