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

The Example-Rich, Hands-On Guide to Data Munging with Apache HadoopTM Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project. Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark. This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop. Coverage includes A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis Assessing tradeoffs in common approaches to imputing missing values Implementing quality checks with Pig or Hive UDFs Transforming raw data into “feature matrix” format for machine learning algorithms Choosing features and instances Implementing text features via “bag-of-words” and NLP techniques Handling time-series data via frequency- or time-domain methods Manipulating feature values to prepare for modeling Data Munging with Hadoop is part of a larger, forthcoming work entitled Data Science Using Hadoop. To be notified when the larger work is available, register your purchase of Data Munging with Hadoop at informit.com/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”

Master D3, Today’s Most Powerful Tool for Visualizing Data on the Web Data-driven graphics are everywhere these days, from websites and mobile apps to interactive journalism and high-end presentations. Using D3, you can create graphics that are visually stunning and powerfully effective. Visual Storytelling with D3 is a hands-on, full-color tutorial that teaches you to design charts and data visualizations to tell your story quickly and intuitively, and that shows you how to wield the powerful D3 JavaScript library. Drawing on his extensive experience as a professional graphic artist, writer, and programmer, Ritchie S. King walks you through a complete sample project—from conception through data selection and design. Step by step, you’ll build your skills, mastering increasingly sophisticated graphical forms and techniques. If you know a little HTML and CSS, you have all the technical background you’ll need to master D3. This tutorial is for web designers creating graphics-driven sites, services, tools, or dashboards; online journalists who want to visualize their content; researchers seeking to communicate their results more intuitively; marketers aiming to deepen their connections with customers; and for any data visualization enthusiast. Coverage includes Identifying a data-driven story and telling it visually Creating and manipulating beautiful graphical elements with SVG Shaping web pages with D3 Structuring data so D3 can easily visualize it Using D3’s data joins to connect your data to the graphical elements on a web page Sizing and scaling charts, and adding axes to them Loading and filtering data from external standalone datasets Animating your charts with D3’s transitions Adding interactivity to visualizations, including a play button that cycles through different views of your data Finding D3 resources and getting involved in the thriving online D3 community About the Website All of this book’s examples are available at ritchiesking.com/book, along with video tutorials, updates, supporting material, and even more examples, as they become available.

Domain-Driven Design (DDD) software modeling delivers powerful results in practice, not just in theory, which is why developers worldwide are rapidly moving to adopt it. Now, for the first time, there’s an accessible guide to the basics of DDD: What it is, what problems it solves, how it works, and how to quickly gain value from it. Concise, readable, and actionable, Domain-Driven Design Distilled never buries you in detail–it focuses on what you need to know to get results. Vaughn Vernon, author of the best-selling Implementing Domain-Driven Design, draws on his twenty years of experience applying DDD principles to real-world situations. He is uniquely well-qualified to demystify its complexities, illuminate its subtleties, and help you solve the problems you might encounter. Vernon guides you through each core DDD technique for building better software. You’ll learn how to segregate domain models using the powerful Bounded Contexts pattern, to develop a Ubiquitous Language within an explicitly bounded context, and to help domain experts and developers work together to create that language. Vernon shows how to use Subdomains to handle legacy systems and to integrate multiple Bounded Contexts to define both team relationships and technical mechanisms. Domain-Driven Design Distilled brings DDD to life. Whether you’re a developer, architect, analyst, consultant, or customer, Vernon helps you truly understand it so you can benefit from its remarkable power. Coverage includes What DDD can do for you and your organization–and why it’s so important The cornerstones of strategic design with DDD: Bounded Contexts and Ubiquitous Language Strategic design with Subdomains Context Mapping: helping teams work together and integrate software more strategically Tactical design with Aggregates and Domain Events Using project acceleration and management tools to establish and maintain team cadence

GRAMMY nominated and Dove Award winning CCM music group Point of Grace offers fans and gift givers alike a beautifully designed, photographically rich, and warmly interactive gift filled with essays, tips, and reflections on the lessons God has taught them over the past twenty years--covering everything from having kids, entering in new jobs, parenting in the hard seasons, contentment, college, marriage, worry, fear, authenticity, health, faith, life transitions, and more!

Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours In just 24 lessons of one hour or less, Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours helps you leverage Hadoop’s power on a flexible, scalable cloud platform using Microsoft’s newest business intelligence, visualization, and productivity tools. This book’s straightforward, step-by-step approach shows you how to provision, configure, monitor, and troubleshoot HDInsight and use Hadoop cloud services to solve real analytics problems. You’ll gain more of Hadoop’s benefits, with less complexity–even if you’re completely new to Big Data analytics. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Practical, hands-on examples show you how to apply what you learn Quizzes and exercises help you test your knowledge and stretch your skills Notes and tips point out shortcuts and solutions Learn how to... · Master core Big Data and NoSQL concepts, value propositions, and use cases · Work with key Hadoop features, such as HDFS2 and YARN · Quickly install, configure, and monitor Hadoop (HDInsight) clusters in the cloud · Automate provisioning, customize clusters, install additional Hadoop projects, and administer clusters · Integrate, analyze, and report with Microsoft BI and Power BI · Automate workflows for data transformation, integration, and other tasks · Use Apache HBase on HDInsight · Use Sqoop or SSIS to move data to or from HDInsight · Perform R-based statistical computing on HDInsight datasets · Accelerate analytics with Apache Spark · Run real-time analytics on high-velocity data streams · Write MapReduce, Hive, and Pig programs Register your book at informit.com/register for convenient access to downloads, updates, and corrections as they become available.

Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters Exploring the Hadoop Distributed File System (HDFS) Understanding the essentials of MapReduce and YARN application programming Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase Observing application progress, controlling jobs, and managing workflows Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark