SQL Server 2012 Data Integration Recipes provides focused and practical solutions to real world problems of data integration. Need to import data into SQL Server from an outside source? Need to export data and send it to another system? SQL Server 2012 Data Integration Recipes has your back. You'll find solutions for importing from Microsoft Office data stores such as Excel and Access, from text files such as CSV files, from XML, from other database brands such as Oracle and MySQL, and even from other SQL Server databases. You'll learn techniques for managing metadata, transforming data to meet the needs of the target system, handling exceptions and errors, and much more. What DBA or developer isn't faced with the need to move data back and forth? Author Adam Aspin brings 10 years of extensive ETL experience involving SQL Server, and especially satellite products such as Data Transformation Services and SQL Server Integration Services. Extensive coverage is given to Integration Services, Microsoft's flagship tool for data integration in SQL Server environments. Coverage is also given to the broader range of tools such as OPENDATASOURCE, linked servers, OPENROWSET, Migration Assistant for Access, BCP Import, and BULK INSERT just to name a few. If you're looking for a resource to cover data integration and ETL across the gamut of Microsoft's SQL Server toolset, SQL Server 2012 Data Integration Recipes is the one book that will meet your needs. Provides practical and proven solutions towards creating resilient ETL environments Clearly answers the tough questions which professionals ask Goes beyond the tools to a thorough discussion of the underlying techniques Covers the gamut of data integration, beyond just SSIS Includes example databases and files to allow readers to test the recipes What you'll learn Import and export to and from CSV files, XML files, and other text-based sources. Move data between SQL databases, including SQL Server and others such as Oracle Database and MySQL. Discover and manage metadata held in various database systems. Remove duplicates and consolidate from multiple sources. Transform data to meet the needs of target systems. Profile source data as part of the discovery process. Log and manage errors and exceptions during an ETL process. Improve efficiency by detecting and processing only changed data. Who this book is for SQL Server 2012 Data Integration Recipes is written for developers wishing to find fast and reliable solutions for importing and exporting to and from SQL Server. The book appeals to DBAs as well, who are often tasked with implementing ETL processes. Developers and DBAs moving to SQL Server from other platforms will find the succinct, example-based approach ideal for quickly applying their general ETL knowledge to the specific tools provided as part of a SQL Server environment.

Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and a concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries. Ideal for data analysts and scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other tools that take scientific programming to a whole new level. The new edition is now available, fully revised and updated in June 2013. Learn the capabilities of NumPy arrays, element-by-element operations, and core mathematical operations Solve minimization problems quickly with SciPy’s optimization package Use SciPy functions for interpolation, from simple univariate to complex multivariate cases Apply a variety of SciPy statistical tools such as distributions and functions Learn SciPy’s spatial and cluster analysis classes Save operation time and memory usage with sparse matrices

Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu. Learn how to use a problem's "weight" against itself to: Break down seemingly complex data problems into simplified parts Use alternative data analysis techniques to examine them Use human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problemsLearn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

To meet today's complex and ever-changing business demands, you need a solid foundation of compute, storage, networking, and software resources. This system must be simple to deploy, and be able to quickly and automatically adapt to changing conditions. You also need to be able to take advantage of broad expertise and proven guidelines in systems management, applications, industry solutions, and more. IBM® PureFlex™ System combines no-compromise system designs along with built-in expertise, and integrates them into complete, optimized scalable solutions. With IBM Flex System™ Manager, multiple solution components that include compute nodes, network and storage infrastructures, storage systems, and heterogeneous virtualization environments can be managed from a single panel. This IBM Redbooks® publication introduces IBM PureFlex System and its management devices and appliances. It provides implementation guidelines for managing Linux KVM, IBM PowerVM®, VMware vSphere and Microsoft Hyper-V virtualization environments. This book is intended for the IT community of clients, Business Partners, and IBM employees who are interested in planning and implementing systems management of the IBM PureFlex System.

Statistics is the branch of mathematics that deals with real-life problems. As such, it is an essential tool for economists. Unfortunately, the way you and many other economists learn the concept of statistics is not compatible with the way economists think and learn. The problem is worsened by the use of mathematical jargon and complex derivations. Here’s a book that proves none of this is necessary. All the examples and exercises in this book are constructed within the field of economics, thus eliminating the difficulty of learning statistics with examples from fields that have no relation to business, politics, or policy. Statistics is, in fact, not more difficult than economics. Anyone who can comprehend economics can understand and use statistics successfully within this field, including you! This book utilizes Microsoft Excel to obtain statistical results, as well as to perform additional necessary computations. Microsoft Excel is not the software of choice for performing sophisticated statistical analysis. However, it is widely available, and almost everyone has some degree of familiarity with it. Using Excel will eliminate the need for students and readers to buy and learn new software, the need that itself would prove to be another impediment to learning and using statistics.

Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts. Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book. Learn basic concepts of measurement and probability theory, data management, and research design Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher’s exact tests, and techniques for analyzing nonparametric data Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology Communicate with statistics and critique statistical information presented by others

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections, each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice. Note: The ebook version does not provide access to the companion files.

Data Mining for Bioinformatics enables researchers to meet the challenge of mining vast amounts of biomolecular data to discover real knowledge. Covering theory, algorithms, and methodologies, as well as data mining technologies, it presents a thorough discussion of data-intensive computations used in data mining applied to bioinformatics. The book explains data mining design concepts to build applications and systems. Showing how to prepare raw data for the mining process, the text is filled with heuristics that speed the data mining process.

An essential reference dedicated to a wide array of financial models, issues in financial modeling, and mathematical and statistical tools for financial modeling The need for serious coverage of financial modeling has never been greater, especially with the size, diversity, and efficiency of modern capital markets. With this in mind, the Encyclopedia of Financial Models, 3 Volume Set has been created to help a broad spectrum of individuals—ranging from finance professionals to academics and students—understand financial modeling and make use of the various models currently available. Incorporating timely research and in-depth analysis, the Encyclopedia of Financial Models is an informative 3-Volume Set that covers both established and cutting-edge models and discusses their real-world applications. Edited by Frank Fabozzi, this set includes contributions from global financial experts as well as academics with extensive consulting experience in this field. Organized alphabetically by category, this reliable resource consists of three separate volumes and 127 entries—touching on everything from asset pricing and bond valuation models to trading cost models and volatility—and provides readers with a balanced understanding of today's dynamic world of financial modeling. This 3-Volume Set contains coverage of the fundamentals and advances in financial modeling and provides the mathematical and statistical techniques needed to develop and test financial models Emphasizes both technical and implementation issues, providing researchers, educators, students, and practitioners with the necessary background to deal with issues related to financial modeling Each volume includes a complete table of contents and index for easy access to various parts of the encyclopedia Financial models have become increasingly commonplace, as well as complex. They are essential in a wide range of financial endeavors, and this 3-Volume Set will help put them in perspective.

Discover how to leverage Talend Open Studio for Data Integration to manage and optimize your data workflow. This book provides a hands-on introduction to creating integration jobs and automating data processes using Talend's drag-and-drop interface. Explore practical examples, and realize how powerful and approachable data integration can be. What this Book will help me do Develop and deploy scalable data integration pipelines using Talend Open Studio. Master common data operations like filtering, sorting, transforming, and aggregating. Gain expertise in connecting various data sources, both relational and non-relational. Implement complex flow logic, including conditional processing and dependencies. Learn to package and manage production-ready integration jobs for real-world scenarios. Author(s) Jonathan Bowen is an experienced technologist and author specializing in data integration and software tools. With years of hands-on experience, Jonathan has guided many organizations in adopting efficient data workflows. He conveys technical concepts with clarity and provides practical, actionable content to help readers succeed. Who is it for? This book is perfect for developers, business analysts, and IT professionals tasked with integration projects. Whether you're a novice to data integration or looking to deepen your hands-on experience with Talend, this guide will support your journey. Some prior familiarity with SQL and a data management background are advantageous. Choose this book if you aim to become a proficient data integrator.

Hands-on troubleshooting methods on the most recent release of SQL Server The 2012 release of SQL Server is the most significant one since 2005 and introduces an abundance of new features. This critical book provides in-depth coverage of best practices for troubleshooting performance problems based on a solid understanding of both SQL Server and Windows internals and shows experienced DBAs how to ensure reliable performance. The team of authors shows you how to master the use of specific troubleshooting tools and how to interpret their output so you can quickly identify and resolve any performance issue on any server running SQL Server. Covers the core technical topics required to understand how SQL Server and Windows should be working Shares best practices so that you know how to proactively monitor and avoid problems Shows how to use tools to quickly gather, analyze, and effectively respond to the source of a system-wide performance issue Professional SQL Server 2012 Internals and Troubleshooting helps you to quickly become familiar with the changes of this new release so that you can best handle database performance and troubleshooting.

The step-by-step visual guide to spotting potential price movements and improving returns Bloomberg Visual Guide to Chart Patterns is a concise and accessible visual guide to identifying, understanding, and using chart patterns to predict the direction and extent of price moves. Packed with visual learning enhancements and exercises, this innovative book helps savvy investors and professionals alike master the essential skills of chart pattern recognition. Follow along as chart pattern expert Thomas Bulkowski teaches you to recognize important peaks and valleys that form patterns—footprints of the smart money. Nearly 200 color charts assist in providing a step-by-step approach to finding those footprints, interpreting them, and following them. Popular patterns such as head-and-shoulders, double tops and bottoms, triangles, gaps, flags, and pennants are just a few of the many patterns explored throughout the book. For the sophisticated trader or investor, the book also provides statistical research to support the claims of pattern behavior, trading signals, and setups, in an easy to understand way. Discusses chart pattern identification guidelines, psychology, variations, failures, and buy and sell signals Covers the most popular and common chart patterns as well as lesser-known ones like throwbacks, pullbacks, and busted patterns Incorporates quizzes, step-by-step exercises, enhanced graphics and video tutorials to immerse the reader in the world of chart patterns Designed for use by investors and traders, from beginners to experts looking for a practical, easy-to-use guide, comprehensive reference, Bloomberg Visual Guide to Chart Patterns provides a sophisticated introduction to the world of chart patterns.

IBM® z/VM® 6.2 introduced significant changes to z/VM with a multi-system clustering technology that allows up to four z/VM instances in a single system image (SSI) cluster. This technology is important because it offers you an attractive alternative to vertical growth by adding new z/VM systems. In the past, this capability required duplicate efforts to install, maintain, and manage each system. With SSI, these duplicate efforts are reduced or eliminated. Support for live guest relocation (LGR) allows you to move Linux virtual servers without disrupting your business or incurring loss of service, thus reducing planned outages. The z/VM systems are aware of each other and take advantage of their combined resources. LGR enables you to relocate guests from a system requiring maintenance to a system that will remain active during maintenance. A major advantage for DB2 v10 customers is that using z/VM 6.2 does not require any changes to existing DB2 structures. This remarkable benefit is due to the fact that DB2 v10 is installed as part of the LInux guest on z/VM and is fully integrated into LGR. This allows you to smoothly move DB2 v10 when you move Linux virtual servers, without interrupting either DB2 v10 or z/VM operations and services. This IBM Redbooks® publication will help you understand how DB2 10 on Linux for System z® behaves while running on a z/VM that is being relocated using z/VM’s 6.2 Live Guest Relocation feature. In this book, we explore memory management, the DB2 Self-tuning memory manager feature, time synchronization, networking, and storage and performance considerations with regards to relocation. We also offer some best practices found during a live guest relocation for DB2 v10.

HBase in Action has all the knowledge you need to design, build, and run applications using HBase. First, it introduces you to the fundamentals of distributed systems and large scale data handling. Then, you'll explore real-world applications and code samples with just enough theory to understand the practical techniques. You'll see how to build applications with HBase and take advantage of the MapReduce processing framework. And along the way you'll learn patterns and best practices. About the Technology HBase is a NoSQL storage system designed for fast, random access to large volumes of data. It runs on commodity hardware and scales smoothly from modest datasets to billions of rows and millions of columns. About the Book HBase in Action is an experience-driven guide that shows you how to design, build, and run applications using HBase. First, it introduces you to the fundamentals of handling big data. Then, you'll explore HBase with the help of real applications and code samples and with just enough theory to back up the practical techniques. You'll take advantage of the MapReduce processing framework and benefit from seeing HBase best practices in action. What's Inside When and how to use HBase Practical examples Design patterns for scalable data systems Deployment, integration, and design About the Reader Written for developers and architects familiar with data storage and processing. No prior knowledge of HBase, Hadoop, or MapReduce is required. About the Authors Nick Dimiduk is a Data Architect with experience in social media analytics, digital marketing, and GIS. Amandeep Khurana is a Solutions Architect focused on building HBase-driven solutions. Quotes Timely, practical ... explains in plain language how to use HBase. - From the Foreword by Michael Stack, Chair of the Apache HBase Project Management Committee A difficult topic lucidly explained. - John Griffin, coauthor of "Hibernate Search in Action" Amusing tongue-in-cheek style that doesn’t detract from the substance. - Charles Pyle, APS Healthcare Learn how to think the HBase way. - Gianluca Righetto, Menttis

Pro SQL Server 2012 Practices Edition is an anthology of high-end wisdom from a group of accomplished database administrators who are quietly but relentlessly pushing the performance and feature envelope of Microsoft SQL Server 2012. With an emphasis upon performance—but also branching into release management, auditing, and other issues—the book helps you deliver the most value for your company's investment in Microsoft's flagship database system Goes beyond the manual to cover good techniques and best practices Delivers knowledge usually gained only by hard experience Focuses upon performance, scalability, reliability Helps achieve the predictability needed to be in control at all times What you'll learn Migrate to the new Extended Events framework for troubleshooting performance problems Automate the tracking of key performance indicators such as available disk space, cpu utilization, index fragmentation, and more Design performance into your database and its applications from the very beginning Manage staged releases from development to test through to production Diagnose resource and performance problems via dynamic management views Analyze I/O patterns and plan reliably to obtain the performance you need Who this book is for Pro SQL Server 2012 Practices is aimed at SQL Server professionals who want to further their careers through expert-level practices in performance management, scalability, predictability, and security.

Expert guidance for building an information communication and technology infrastructure that provides best in business intelligence Enterprise performance management (EPM) technology has been rapidly advancing, especially in the areas of predictive analysis and cloud-based solutions. Business intelligence caught on as a concept in the business world as the business strategy application of data warehousing in the early 2000s. With the recent surge in interest in data analytics and big data, it has seen a renewed level of interest as the ability of a business to find the valuable data in a timely—and competitive—fashion. Business Intelligence Applied reveals essential information for building an optimal and effective information and communication technology (ICT) infrastructure. Defines ICT infrastructure Examines best practices for documenting business change and for documenting technology recommendations Includes examples and cases from Europe and Asia Written for business intelligence staff, CIOs, CTOs, and technology managers With examples and cases from Europe and Asia, Business Intelligence Applied expertly covers business intelligence, a hot topic in business today as a key element to business and data analytics.

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models.