Includes referencias bibliográficas e índice.

I. Directives in the big data era --
1. Four rules for data success --

II. Collecting and sharing a lot of data --
2. Hosting and sharing terabytes of raw data --
3. Building a noSQL-based web app to collect crowd-sourced data --
4. Strategies for dealing with data silos --

III. Asking questions about your data --
5. Using hadoop, hive, and shark to ask questions about large datasets --
6. Building a data dashboard with Google bigquery --
7. Visualization strategies for exploring large datasets --

IV. Building data pipelines --
8. Putting it together: mapreduce data pipelines --
9. Building data transformation workflows with pig and cascading --

V. Machine learning for large datasets --
10. Building a data classification system with mahout --

VI. Statistical analysis for massive datasets --
11. Using R with large datasets --
12. Building analytics workflows using python and pandas --

VII. Looking ahead --
13. When to buid, when to buy, when to outsource --
14. The future: trends in data technology --

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 Proporcionado por el editor.

El análisis de datos a gran escala es ahora de vital importancia para prácticamente todas las empresas. Las tecnologías móviles y sociales están generando conjuntos de datos masivos; la computación en la nube distribuida ofrece los recursos para almacenarlos y analizarlos; y los profesionales tienen a su disposición tecnologías radicalmente nuevas, incluidas las bases de datos NoSQL. Sin embargo, hasta ahora, la mayoría de los libros sobre “Big Data” han sido poco más que polémicas comerciales o catálogos de productos. Data Just Right es diferente: es una guía completamente práctica e indispensable para todos los tomadores de decisiones, implementadores y estrategas de Big Data.



Michael Manoochehri, ex ingeniero de Google y hacker de datos, escribe para profesionales que necesitan soluciones prácticas que puedan implementarse con recursos y tiempo limitados. Aprovechando su amplia experiencia, lo ayuda a concentrarse en crear aplicaciones, en lugar de infraestructura, porque ahí es donde puede obtener el mayor valor.



Manoochehri muestra cómo abordar cada uno de los casos de uso clave de Big Data de hoy en día de una manera rentable mediante la combinación de tecnologías en soluciones híbridas. Encontrará enfoques expertos para administrar conjuntos de datos masivos, visualizar datos, crear canalizaciones y tableros de datos, elegir herramientas para análisis estadístico y más. En todo momento, el autor demuestra técnicas que utilizan muchas de las principales herramientas de análisis de datos de la actualidad, incluidas Hadoop, Hive, Shark, R, Apache Pig, Mahout y Google BigQuery.