Abstract This IBM® Redbooks® publication describes the IBM System z® Personal Development Tool (IBM zPDT®) 2017 Sysplex Extensions, which is a package that consists of sample files and supporting documentation to help you get a functioning, data sharing sysplex up and running with minimal time and effort. This book is a significant revision of zPDT 2016 Sysplex Extensions, SG24-8315, which is still available online for readers who need the IBM z/OS® 2.1 level of this package. This package is designed and tested to be installed on top of a standard Application Developers Controlled Distribution (ADCD) environment. It provides the extra files that you need to create a two-way data sharing IBM z/OS 2.2 sysplex that runs under IBM z/VM® in a zPDT environment.

See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource management, processing, queuing, frameworks, data visualization, and more. What You’ll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for anyone interested in Hadoop or big data, and those experiencing problems with data size.

Abstract This IBM® Redbooks® publication is intended for IBM CICS® system programmers and IBM Z architects. It describes how to deploy and manage Java EE 7 web-based applications in an IBM CICS Liberty JVM server and access data on IBM Db2® for IBM z/OS® and IBM MQ for z/OS sub systems. In this book, we describe the key steps to create and install a Liberty JVM server within a CICS region. We then describe how to best use the different deployment techniques for Java EE applications and the specific considerations when deploying applications that use JDBC, JMS, and the new CICS link to Liberty API. Finally, we describe how to secure web applications in CICS Liberty, including transport-level security and request authentication and authorization by using IBM RACF® and LDAP registries. Information is also provided about how to build a high availability infrastructure and how to use the logging and monitoring functions that are available in the CICS Liberty environment. This book is based on IBM CICS Transaction Server (CICS TS) V5.4 that uses the embedded IBM WebSphere® Application Server Liberty technology. It is also applicable to CICS TS V5.3 with the fixes for the continuous delivery APAR PI77502 applied. Sample applications are used throughout this publication and are freely available for download from the IBM CICSDev GitHub organization along with detailed deployment instructions.

With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online. This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.

In this lesson, you'll learn the guiding principles of excellent database reliability engineering. You can apply this framework of practices for designing, building, and operating data stores to any database technology or environment.

Practical Big Data Analytics is your ultimate guide to harnessing Big Data technologies for enterprise analytics and machine learning. By leveraging tools like Hadoop, Spark, NoSQL databases, and frameworks such as R, this book equips you with the skills to implement robust data solutions that drive impactful business insights. Gain practical expertise in handling data at scale and uncover the value behind the numbers. What this Book will help me do Master the fundamental concepts of Big Data storage, processing, and analytics. Gain practical skills in using tools like Hadoop, Spark, and NoSQL databases for large-scale data handling. Develop and deploy machine learning models and dashboards with R and R Shiny. Learn strategies for creating cost-efficient and scalable enterprise data analytics solutions. Understand and implement effective approaches to combining Big Data technologies for actionable insights. Author(s) None Dasgupta is an expert in Big Data analytics, statistical methodologies, and enterprise data solutions. With years of experience consulting on enterprise data platforms and working with leading industry technologies, Dasgupta brings a wealth of practical knowledge to help readers navigate and succeed in the field of Big Data. Through this book, Dasgupta shares an accessible and systematic way to learn and apply key Big Data concepts. Who is it for? This book is ideal for professionals eager to delve into Big Data analytics, regardless of their current level of expertise. It accommodates both aspiring analysts and seasoned IT professionals looking to enhance their knowledge in data-driven decision making. Individuals with a technical inclination and a drive to build Big Data architectures will find this book particularly beneficial. No prior knowledge of Big Data is required, although familiarity with programming concepts will enhance the learning experience.

In-memory data grids (IMDG) such as Pivotal GemFire, which is powered by Apache Geode, are key to making today’s modern high-speed, data-intensive applications work. By keeping data in the RAM of a horizontally scalable cluster of servers, IMDG solutions enable apps to achieve consistently low latency for data access at any scale. Many in the application development community, however, aren’t aware of IMDG’s benefits, use cases, or underlying technology. This report brings you up to speed by providing GemFire basics, including use cases and easily understood examples. You’ll determine whether GemFire can benefit your application, and learn how to install a simple test environment and build a small proof of concept. Explore GemFire use cases for Java applications—including microservices, high-speed data ingest, and transaction and event processing Get an architectural overview of GemFire, and learn installation requirements for both hardware/VM and cloud Dive into GemFire’s capabilities with continuous queries, server-side functions, and Apache Lucene integration Learn how GemFire works with the persistence model, off-heap memory, and WAN replication

Abstract This IBM® Redbooks® publication helps you with the planning, installation, and configuration of the new IBM Spectrum™ Archive V1.2.5.1 for the IBM TS3310, IBM TS3500, IBM TS4300, and IBM TS4500 tape libraries. IBM Spectrum Archive™ EE enables the use of the LTFS for the policy management of tape as a storage tier in an IBM Spectrum Scale™ based environment. It helps encourage the use of tape as a critical tier in the storage environment. This is the fifth edition of IBM Spectrum Archive Installation and Configuration Guide. IBM Spectrum Archive EE can run any application that is designed for disk files on a physical tape media. IBM Spectrum Archive EE supports the IBM Linear Tape-Open (LTO) Ultrium 8, 7, 6, and 5 tape drives in IBM TS3310, TS3500, TS4300, and TS4500 tape libraries. In addition, IBM TS1155, TS1150, and TS1140 tape drives are supported in TS3500 and TS4500 tape library configurations. IBM Spectrum Archive EE can play a major role in reducing the cost of storage for data that does not need the access performance of primary disk. The use of IBM Spectrum Archive EE to replace disks with physical tape in tier 2 and tier 3 storage can improve data access over other storage solutions because it improves efficiency and streamlines management for files on tape. IBM Spectrum Archive EE simplifies the use of tape by making it transparent to the user and manageable by the administrator under a single infrastructure. This publication is intended for anyone who wants to understand more about IBM Spectrum Archive EE planning and implementation. This book is suitable for IBM clients, IBM Business Partners, IBM specialist sales representatives, and technical specialists.

Prepare for the SAS Base Programming for SAS 9 exam with the official guide by the SAS Global Certification Program. New and experienced SAS users who want to prepare for the SAS Base Programming for SAS 9 exam will find this guide to be an invaluable, convenient, and comprehensive resource that covers all of the objectives tested on the exam. Now in its fourth edition, the guide has been extensively updated, and revised to streamline explanations. Major topics include importing and exporting raw data files, creating and modifying SAS data sets, and identifying and correcting data syntax and programming logic errors. The chapter quizzes have been thoroughly updated and full solutions are included at the back of the book. In addition, links are provided to the exam objectives, practice exams, and other helpful resources, such as the updated Base SAS glossary and an expanded collection of practice data sets. Content updates are available here.

Abstract With the advances of technology and the reoccurrence of data leaks, cyber security is a bigger challenge than ever before. Cyber attacks evolve as quickly as the technology itself, and hackers are finding more innovative ways to break security controls to access confidential data and to interrupt services. Hackers reinvent themselves using new technology features as a tool to expose companies and individuals. Therefore, cyber security cannot be reactive but must go a step further by implementing proactive security controls that protect one of the most important assets of every organization: the company's information. This IBM® Redbooks® publication provides information about implementing IBM QRadar® for Security Intelligence and Event Monitoring (SIEM) and protecting an organization's networks through a sophisticated technology, which permits a proactive security posture. It is divided in to the following major sections to facilitate the integration of QRadar with any network architecture: Chapter 2, "Before the installation" on page 3 provides a review of important requirements before the installation of the product. Chapter 3, "Installing IBM QRadar V7.3" on page 57 provides step-by-step procedures to guide you through the installation process. Chapter 4, "After the installation" on page 77 helps you to configure additional features and perform checks after the product is installed. QRadar is an IBM Security prime product that is designed to be integrated with corporate network devices to keep a real-time monitoring of security events through a centralized console. Through this book, any network or security administrator can understand the product's features and benefits.

This book provides an updated, concise summary of forecasting air travel demand methodology. It looks at air travel demand forecasting research and outlines the intellectual landscape of demand forecasting. The book also discusses what to do when facing different forecasting problems.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Perform Interactive Data Analysis with Smarter Visualizations and Support your Enterprise-wide Analytical Needs About This Book Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Who This Book Is For This course is for anyone who wishes to understand and utilize the various new approaches to business intelligence actively in their business practice. Knowing the basics of business intelligence concepts would be helpful when picking up this course, but is not mandatory. What You Will Learn Build simple visualization models with Rattle and Qlik Sense Desktop Get to grips with the life cycle and new visualization functions of a Qlik Sense application Discover simple ways to examine data and get it ready for analysis Visualize your data with Qlik Sense's engaging and informative graphs Build efficient and responsive Associative Models Optimize Qlik Sense for sales, human resources, and demographic data discovery Explore various tips and tricks of navigation for the Qlik Sense® front end Develop creative extensions for your Qlik Sense® dashboard In Detail Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business. This course begins by introducing you to the features and functions of the most modern edition of Qlik Sense so you get to grips with the application. The course will teach you how to administer the data architecture in Qlik Sense, enabling you to customize your own Qlik Sense application for your business intelligence needs. It also contains numerous recipes to help you overcome challenging situations while creating fully featured desktop applications in Qlik Sense. It explains how to combine Rattle and Qlik Sense Desktop to apply predictive analytics to your data to develop real-world interactive data applications. The course includes premium content from three of our most popular books: Learning Qlik Sense: The Official Guide Second Edition Qlik Sense Cookbook Predictive Analytics using Rattle and Qlik Sense On completion of this course, you will be self-sufficient in improving your data analysis and will know how to apply predictive analytics to your datasets. Through this course, you will be able to create predictive models and data applications, allowing you to explore your data insights much deeper. Style and approach The course will follow a practical approach with rich set of examples through which it will demonstrate its concepts, features and its implementation. The course will also feature numerous solutions which will cover entire spectrum of BI use cases.

Learn how to leverage IBM SPSS Modeler for your data mining and predictive analytics needs in this comprehensive guide. With step-by-step instructions, you'll acquire the skills to import, clean, analyze, and model your data using this robust platform. By the end, you'll be equipped to uncover patterns and trends, enabling data-driven decision-making confidently. What this Book will help me do Understand the fundamentals of data mining and the visual programming interface of IBM SPSS Modeler. Prepare, clean, and preprocess data effectively for analysis and modeling. Build robust predictive models such as decision trees using best practices. Evaluate the performance of your analytical models to ensure accuracy and reliability. Export resulting analyses to apply insights to real-world data projects. Author(s) Keith McCormick and Jesus Salcedo are accomplished professionals in data analytics and statistical modeling. With extensive experience in consulting and teaching, they have guided many in mastering IBM SPSS Modeler through both hands-on workshops and written material. Their approachable teaching style and commitment to clarity ensure accessibility for learners. Who is it for? This book is designed for beginner users of IBM SPSS Modeler who wish to gain practical and actionable skills in data analytics. If you're a data enthusiast looking to explore predictive analytics or a professional eager to discover the insights hidden in your organizational data, this book is for you. A basic understanding of data mining concepts is advantageous but not required. This resource will set any novice on the path toward expert-level comprehension and application.

Learning Alteryx introduces you to using the powerful Alteryx platform for self-service analytics, helping you master key features like data preparation and predictive analytics without needing to code. With this book, you'll gain the skills to create workflows that generate actionable insights, empowering your business to make data-driven decisions. What this Book will help me do Master creating and optimizing workflows in Alteryx to address complex analytical problems. Learn how to clean, prepare, and blend data from various sources efficiently. Understand advanced Alteryx expressions for processing large datasets effectively. Develop meaningful reports and visualizations to communicate insights clearly. Leverage predictive analytics capabilities in Alteryx to make informed decisions. Author(s) The authors of Learning Alteryx collectively bring years of expertise in data analytics and business intelligence. Having worked on diverse projects across multiple industries, they understand the challenges faced by data professionals and are skilled in simplifying complex concepts. They focus on providing practical insights and step-by-step guides to empower learners. Who is it for? Learning Alteryx is ideal for professionals aspiring to enhance their data analytics capabilities or explore self-service analytics. It caters to beginners unfamiliar with analytics platforms, as well as intermediate users seeking to deepen their Alteryx knowledge. Readers should have a basic understanding of data analysis principles.

Dive into the essential resource for mastering Apache Kafka with this cookbook of practical recipes. You'll explore the dynamic features of Kafka 1.0, integrate it with enterprise data solutions, and confidently manage messaging and streaming data in real-time. What this Book will help me do Effectively install and configure Apache Kafka in a professional environment. Implement Kafka producers and consumers to manage real-time data streams. Utilize Confluent platforms and Kafka streams for advanced data processing. Monitor Kafka clusters with tools like Graphite and Ganglia for optimal performance. Integrate Kafka seamlessly with tools such as Hadoop, Spark, and Elasticsearch. Author(s) None Estrada and None Zinoviev have extensive experience in enterprise data systems and have been dedicated contributors to the Apache Kafka ecosystem. Their combined expertise encompasses developing robust, real-time distributed systems and delivering insightful technical guidance. Through this book, they share their vast knowledge and practical solutions, tailored for both developers and administrators. Who is it for? This book is tailored for developers and administrators looking to enhance their expertise in Apache Kafka. Developers should be comfortable with Java or Scala to fully utilize examples, while administrators benefit from prior knowledge of Kafka operations. Ideal readers are those seeking actionable techniques to efficiently manage and integrate Kafka into their enterprise systems.

Abstract This IBM® Redbooks® publication covers the background and implementation of the IBM CICS® asynchronous API, which is a simple, accessible API that is designed to enable CICS application developers to create efficient asynchronous programs in all CICS-supported languages. Using the API, application developers can eliminate the overhead that is involved in coding and managing homegrown asynchronous solutions, instead using a set of CICS-supported API commands to underpin CICS applications, which are more responsive and robust than ever. Initially, the book reviews the history and motivations of asynchronous processing in computing and the benefits involved when calling external services. It then introduces the asynchronous API itself and its commands. It also provides a range of scenarios, including sample code, that cover everything from the basics of making an asynchronous request to updating existing synchronous program calls, with the goal of illustrating how to harness the CICS asynchronous API to solve real business problems. Later chapters take a deeper dive into the capabilities of the asynchronous API for advanced use cases. Beyond application development, CICS provides a complete solution for system programmers to manage and monitor asynchronous business logic. Thus, the final chapters of this book cover enhancements to CICS monitoring, statistics, trace, and dumps. Using supporting CICS tooling, system programmers have greater insight than ever, with improved transaction tracking capabilities and CICS policies to provide maximum control and optimization of asynchronous processing in CICS environments.

Learn how to harness the power of the Elastic Stack 6.0 to manage, analyze, and visualize data effectively. This book introduces you to Elasticsearch, Logstash, Kibana, and other components, helping you build scalable, real-time data processing solutions from scratch. By reading this guide, you'll gain practical insights into the platform's components, including tips for production deployment. What this Book will help me do Understand and utilize the core components of Elastic Stack 6.0, including Elasticsearch, Logstash, and Kibana. Set up scalable data pipelines for ingesting and processing vast amounts of data. Craft real-time data visualizations and analytics using Kibana. Secure and monitor Elastic Stack deployments with X-Pack and other related tools. Deploy Elastic Stack applications effectively in cloud or on-premise production environments. Author(s) Pranav Shukla and Sharath Kumar are experienced professionals with deep knowledge in distributed data systems and the Elastic Stack ecosystem. They are passionate about data analytics and visualization and bring their hands-on experience in building real-world Elastic Stack applications into this book. Their practical approach and explanatory style make complex concepts accessible to readers at all levels. Who is it for? This book is perfect for data professionals who want to analyze large datasets or create effective real-time visualizations. It is suited for those new to Elastic Stack or looking to understand its capabilities. Basic JSON knowledge is recommended, but no prior expertise with Elastic Stack is required to benefit from this practical guide.

If you're ready to untap the potential of data analytics in the cloud, 'Learning Google BigQuery' will take you from understanding foundational concepts to mastering advanced techniques of this powerful platform. Through hands-on examples, you'll learn how to query and analyze massive datasets efficiently, develop custom applications, and integrate your results seamlessly with other tools. What this Book will help me do Understand the fundamentals of Google Cloud Platform and how BigQuery operates within it. Migrate enterprise-scale data seamlessly into BigQuery for further analytics. Master SQL techniques for querying large-scale datasets in BigQuery. Enable real-time data analytics and visualization with tools like Tableau and Python. Learn to create dynamic datasets, manage partition tables and use BigQuery APIs effectively. Author(s) None Berlyant, None Haridass, and None Brown are specialists with years of experience in data science, big data platforms, and cloud technologies. They bring their expertise in data analytics and teaching to make advanced concepts accessible. Their hands-on approach and real-world examples ensure readers can directly apply the skills they acquire to practical scenarios. Who is it for? This book is tailored for developers, analysts, and data scientists eager to leverage cloud-based tools for handling and analyzing large-scale datasets. If you seek to gain hands-on proficiency in working with BigQuery or want to enhance your organization's data capabilities, this book is a fit. No prior BigQuery knowledge is needed, just a willingness to learn.

"R Programming By Example" serves as an engaging and practical introduction to the R programming language for data analysis and visualization. Through step-by-step examples and comprehensive guides, this book builds your understanding from foundational knowledge to advanced applications in R. You will master programming practices while analyzing real-world scenarios. What this Book will help me do Gain proficiency in leveraging R's versatile features and package ecosystem to tackle data analysis tasks. Learn to create and customize high-quality visualizations, including 3D graphs, for enhanced data presentation. Understand statistical modeling and descriptive analysis techniques for extracting insights from data. Discover efficient programming strategies in R, including code profiling and parallelization, to optimize performance. Acquire the skills to interface R with databases and RESTful APIs for robust data integration. Author(s) The authors, None Trejo Navarro and Omar Trejo Navarro, bring a wealth of experience in statistical programming and data analysis. Having worked extensively with R, they focus on practical and results-driven teaching. They have a passion for making complex topics accessible to learners. Who is it for? This book is aimed at aspiring data scientists, statisticians, or analysts looking to learn R. It is particularly suitable for readers familiar with basic programming concepts and who wish to apply R in practical scenarios. Whether you're analyzing data, building models, or creating visualizations, this book will guide you effectively. If you're eager to advance your R skills through hands-on projects, this is for you.