{"product_id":"9780133359077","title":"Data Just Right: Introduction to Large-Scale Data \u0026 Analytics","description":"\u003cp\u003e \u003cb\u003eMaking Big Data Work: Real-World Use Cases and Examples, Practical Code, Detailed Solutions\u003c\/b\u003e \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eLarge-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. \u003cb\u003e \u003ci\u003e \u003cb\u003eData Just Right\u003c\/b\u003e \u003c\/i\u003e \u003c\/b\u003e is different: It’s a completely practical and indispensable guide for every Big Data decision-maker, implementer, and strategist.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eMichael 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.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eManoochehri 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.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eCoverage includes\u003c\/p\u003e  \u003cul\u003e  \u003cli\u003e  Mastering the four guiding principles of Big Data success—and avoiding common pitfalls \u003c\/li\u003e  \u003cli\u003e  Emphasizing collaboration and avoiding problems with siloed data \u003c\/li\u003e  \u003cli\u003e  Hosting and sharing multi-terabyte datasets efficiently and economically \u003c\/li\u003e  \u003cli\u003e  “Building for infinity” to support rapid growth \u003c\/li\u003e  \u003cli\u003e  Developing a NoSQL Web app with Redis to collect crowd-sourced data \u003c\/li\u003e  \u003cli\u003e  Running distributed queries over massive datasets with Hadoop, Hive, and Shark \u003c\/li\u003e  \u003cli\u003e  Building a data dashboard with Google BigQuery \u003c\/li\u003e  \u003cli\u003e  Exploring large datasets with advanced visualization \u003c\/li\u003e  \u003cli\u003e  Implementing efficient pipelines for transforming immense amounts of data \u003c\/li\u003e  \u003cli\u003e  Automating complex processing with Apache Pig and the Cascading Java library \u003c\/li\u003e  \u003cli\u003e  Applying machine learning to classify, recommend, and predict incoming information \u003c\/li\u003e  \u003cli\u003e  Using R to perform statistical analysis on massive datasets \u003c\/li\u003e  \u003cli\u003e  Building highly efficient analytics workflows with Python and Pandas \u003c\/li\u003e  \u003cli\u003e  Establishing sensible purchasing strategies: when to build, buy, or outsource \u003c\/li\u003e  \u003cli\u003e  Previewing emerging trends and convergences in scalable data technologies and the evolving role of the Data Scientist  \u003c\/li\u003e \u003c\/ul\u003e","brand":"Pearson Education","offers":[{"title":"Default Title","offer_id":47147792007408,"sku":"9780133359077","price":29.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9780133359077_p0.jpg?v=1763642281","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9780133359077","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}