Expert SQL Server In-Memory OLTP is a deep dive into one of the most significant features of SQL Server 2014 – support for In-Memory Online Transaction Processing. The book describes the architecture and internals of the In-Memory OLTP Engine and explains how to develop, deploy, and maintain systems using it. With it you can dramatically increase transactional throughput to handle thousands of transactions per second supporting millions of customers. Dmitri Korotkevitch is the five-star author of Pro SQL Server Internals, and now brings his same combination of clear thinking and deep expertise to the question of how to recognize the opportunities and benefit from Microsoft’s In-Memory OLTP feature set in SQL Server 2014. Learn the architecture and the internals in order to recognize when in-memory OLTP can make a difference. Learn useful scenarios for thoughtfully incorporating In-Memory support into existing applications. Recognize opportunities for In-Memory OLTP in new development. Don’t be without Dmitri Korotkevitch and the deep expertise he imparts in Expert SQL Server In-Memory OLTP as you move forward in using SQL Server’s new and important In-Memory OLTP feature set. Covers In-Memory OLTP internals and architecture, including data storage, indexing, multi-version concurrency control, transaction logging, and recovery Illustrates In-Memory OLTP programmability and the process of native compilation Guides in using In-Memory OLTP in new development and existing systems.

Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. Learn how to quickly define scope and architecture before programming starts Includes techniques of process and data engineering that enable iterative and incremental delivery Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges Use the provided 120-day road map to establish a robust, agile data warehousing program

Learn how to use Hibernate, a powerful Java object-relational mapping framework, with the "Java Hibernate Cookbook." This book is designed to help developers quickly grasp the capabilities of Hibernate, taking you from installation to building real-world applications. What this Book will help me do Install and configure Hibernate for Java development. Utilize annotations to streamline Hibernate configuration. Implement collection mappings such as lists, maps, and sets. Understand and create associations between different entities. Integrate Hibernate with other frameworks to develop enterprise solutions. Author(s) None Prajapati and None Ranapariya are experienced Java developers and authors with a passion for sharing knowledge about cutting-edge frameworks and tools. They have significant expertise with Hibernate and have distilled their experience into this comprehensive guide to help developers understand and utilize Hibernate effectively. Who is it for? This book is ideal for Java developers looking to enhance their understanding of Hibernate. It assumes readers are comfortable with Java programming and have a basic knowledge of SQL. Whether you're improving existing skills or aiming to learn Hibernate for a new project, this book provides clear explanations and structured examples to help you succeed.

Learn how to create interactive and stylish web mapping applications with "Mastering ArcGIS Server Development with JavaScript." By focusing on the ArcGIS JavaScript API along with modern development practices, this book provides you with practical examples and insights to build functional, visually appealing GIS applications for the web. What this Book will help me do Integrate and display data from multiple sources into a single-page application using mapping tools. Customize widgets and maps to ensure user engagement and optimal functionality. Use charts and graphs to intuitively represent geographic data insights. Combine the ArcGIS JavaScript API with popular JavaScript libraries for advanced map-making features. Develop web maps suitable for multiple devices and test for platform compatibility. Author(s) None Doman is a seasoned GIS developer with extensive experience in the ArcGIS ecosystem. Specializing in integrating cutting-edge JavaScript techniques with GIS solutions, Doman focuses on empowering developers through comprehensive and insightful learning materials. Their approachable writing style ensures even complex concepts are accessible and engaging. Who is it for? This book is aimed at professional developers looking to enhance their GIS application development skills. Ideal readers have a fundamental understanding of ArcGIS Server and some experience with developing using JavaScript. It is especially beneficial for those eager to apply GIS solutions in web development scenarios. It's perfect for those ready to deepen their expertise in GIS programming and create impactful web mapping applications.

An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0

As any child with a baseball card intuitively knows, sports and statistics go hand-in-hand. Yet, the general media disdain the flood of sports statistics available today: sports are pure and analytic tools are not. Well, if the so-called purists find tools like baseball’s sabermetrics upsetting, then they’d better brace themselves for the new wave of data analytics. In this O’Reilly report, Janine Barlow examines how advanced predictive analytics are impacting the world of sports—from the rise of tools such as Major League Baseball’s Statcast, which collects data on the movement of balls and players, to SportVU, which the National Basketball Association uses to collect spatial analysis data. You’ll also learn: How "Dance Card" makes accurate predictions about NCAA’s "March Madness" tournament Why data is crumbling long-standing myths about performance in soccer How the National Football League is using wearable devices to collect vital health data about its players It’s a new world in sports, where data analytics and related information technologies are changing the experience for teams, players, fans, and investors.

Over the last 20 years, companies have invested roughly $3-4 trillion in enterprise software. These investments have been primarily focused on the development and deployment of single systems, applications, functions, and geographies targeted at the automation and optimization of key business processes. Companies are now investing heavily in big data analytics ($44 billion alone in 2014) in an effort to begin analyzing all of the data being generated from their process automation systems. But companies are quickly realizing that one of their key bottlenecks is Data Variety—the silo’d nature of the data that is a natural result of internal and external source proliferation. The problem of big data variety has crept up from the bottom—and the cost of variety is only appreciated when companies attempt to ask simple questions across many business silos (divisions, geographies, functions, etc.). Current top-down, deterministic data unification approaches (such as ETL, ELT, and MDM) were simply not designed to scale to the variety of hundreds or thousands or even tens of thousands of data silos. Download this free eBook to learn about the fundamental challenges that Data Variety poses to enterprises looking to maximize the value of their existing investments—and how new approaches promise to help organizations embrace and leverage the fundamental diversity of data. Readers will also find best practices for designing bottom-up and probabilistic methods for finding and managing data; principles for doing data science at scale in the big data era; preparing and unifying data in ways that complement existing systems; optimizing data warehousing; and how to use “data ops” to automate large-scale integration.

This IBM® Redbooks® publication documents and addresses topics to provide step-by-step programming concepts to tune the applications to use IBM POWER8® hardware architecture with the technical computing software stack. This publication explores, tests, and documents how to implement an IBM high-performance computing (HPC) solution on POWER8 by using IBM technical innovations to help solve challenging scientific, technical, and business problems. This book demonstrates and documents that the combination of IBM HPC hardware and software solutions delivers significant value to technical computing clients in need of cost-effective, highly scalable, and robust solutions. This book targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for delivering cost-effective HPC solutions that help uncover insights among clients' data so that they can act to optimize business results, product development, and scientific discoveries.

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Organizations across many industries have recently created fast-growing repositories to deal with an influx of new data from many sources and often in multiple formats. To manage these data lakes, companies have begun to leave the familiar confines of relational databases and data warehouses for Hadoop and various big data solutions. But adopting new technology alone won’t solve the problem. Based on interviews with several experts in data management, author Andy Oram provides an in-depth look at common issues you’re likely to encounter as you consider how to manage business data. You’ll explore five key topic areas, including: Acquisition and ingestion: how to solve these problems with a degree of automation. Metadata: how to keep track of when data came in and how it was formatted, and how to make it available at later stages of processing. Data preparation and cleaning: what you need to know before you prepare and clean your data, and what needs to be cleaned up and how. Organizing workflows: what you should do to combine your tasks—ingestion, cataloging, and data preparation—into an end-to-end workflow. Access control: how to address security and access controls at all stages of data handling. Andy Oram, an editor at O’Reilly Media since 1992, currently specializes in programming. His work for O'Reilly includes the first books on Linux ever published commercially in the United States.

To discover the shape and structure of the big data market, the San Francisco-based startup Relato took a unique approach to market research and created the first fully data-driven market report. Company CEO Russell Jurney and his team collected and analyzed raw data from a variety of sources to reveal a boatload of business insights about the big data space. This exceptional report is now available for free download. Using data analytic techniques such as social network analysis (SNA), Relato exposed the vast and complex partnership network that exists among tens of thousands of unique big data vendors. The dataset Relato collected is centered around Cloudera, Hortonworks, and MapR, the major platform vendors of Hadoop, the primary force behind this market. From this snowball sample, a 2-hop network, the Relato team was able to answer several questions, including: Who are the major players in the big data market? Which is the leading Hadoop vendor? What sectors are included in this market and how do they relate? Which among the thousands of partnerships are most important? Who’s doing business with whom? Metrics used in this report are also visible in Relato’s interactive web application, via a link in the report, which walks you through the insights step-by-step.

Compared to the speed and convenience of major web search engines, most business intelligence (BI) products are slow, stiff, and unresponsive. Business leaders today often wait days or weeks to get BI reports on inquiries about customers, products, or markets. But the latest BI products show that a significant change is taking place—a change led by search. This O’Reilly report examines three recent products with intelligent search capabilities: the ThoughtSpot Analytical Search Appliance, Microsoft’s Power BI service, and an offering from Adatao. You’ll learn how these products can provide you with answers and visualizations as quickly as questions come to mind. You’ll investigate: The convergence of BI and search What a search-driven user experience looks like The intelligence required for analytical search Data sources and their associated data modeling requirements Turning on-the-fly calculations into visualizations Applying enterprise scale and security to search

Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

Dive into the world of large-scale graph data processing with Apache Spark's GraphX API. This book introduces you to the core concepts of graph analytics and teaches you how to leverage Spark for handling and analyzing massive graphs. From building to analyzing, you'll acquire a comprehensive skillset to work with graph data efficiently. What this Book will help me do Learn to utilize Apache Spark GraphX API to process and analyze graph data. Master transforming raw datasets into sophisticated graph structures. Explore visualization and analysis techniques for understanding graphs. Understand and build custom graph operations tailored to your needs. Implement advanced graph algorithms like clustering and iterative processing. Author(s) Rindra Ramamonjison is a seasoned data engineer with vast experience in big data technologies and graph processing. With a passion for explaining complex concepts in simple terms, Rindra builds on his professional expertise to guide readers in mastering cutting-edge Spark tools. Who is it for? This book is tailored for data scientists and software developers looking to delve into graph data processing at scale. Ideal for those with basic knowledge of Scala and Apache Spark, it equips readers with the tools and techniques to derive insights from complex network datasets. Whether you're diving deeper into big data or exploring graph-specific analytics, this book is your guide.

Be more productive with the Oracle PL/SQL language. The fifth edition of this popular pocket reference puts the syntax of specific PL/SQL language elements right at your fingertips, including features added in Oracle Database 12c. Whether you’re a developer or database administrator, when you need answers quickly, the Oracle PL/SQL Language Pocket Reference will save you hours of frustration with concise summaries of: Fundamental language elements, such as block structure, datatypes, and declarations Statements for program control, cursor management, and exception handling Records, procedures, functions, triggers, and packages Execution of PL/SQL functions in SQL Compilation options, object-oriented features, collections, and Java integration This handy pocket reference is a perfect companion to Steven Feuerstein and Bill Pribyl’s bestselling Oracle PL/SQL Programming.

A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Professionals in all areas - business; government; the physical, life, and social sciences; engineering; medicine, etc. - benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts. Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

Redis Essentials is your go-to guide for understanding and mastering Redis, the leading in-memory data structure store. In this book, you will explore the powerful features offered by Redis, such as real-time data processing, highly scalable architectures, and practical implementations for web applications. You'll complete the journey equipped to handle and optimize Redis for your development projects. What this Book will help me do Design analytics applications with advanced data structures like Bitmaps and HyperLogLogs. Scale your application infrastructure using Redis Sentinel, Twemproxy, and Redis Cluster. Develop custom Redis commands and extend its functionality with the Lua scripting language. Implement robust security measures for Redis, including SSL encryption and firewall rules. Master the usage of Redis client libraries in PHP, Python, Node.js, and Ruby for seamless development. Author(s) Maxwell Dayvson da Silva is an experienced software engineer and author with expertise in designing high-performance systems. With a strong focus on practical knowledge and hands-on solutions, Maxwell brings over a decade of experience using Redis to this book. His approachable teaching style ensures learners grasp complex topics easily while emphasizing their practical application to real-world challenges. Who is it for? Redis Essentials is aimed at developers looking to enhance their system's performance and scalability using Redis. Whether you're moderately familiar with key-value stores or new to Redis, this book will provide the explanations and hands-on examples you need. Recommended for developers with experience in data architectures, the book bridges the gap between understanding Redis features and their real-world application. Start here to bring high-performance in-memory data solutions to your projects.