Over 50 recipes to learn how to use Pentaho Analytics and MongoDB to create powerful analysis and reporting solutions About This Book Create reports and stunning dashboards with MongoDB data Accelerate data access and maximize productivity with unique features of Pentaho for MongoDB A step-by-step recipe-based guide for making full use of Pentaho suite tools with MongoDB Who This Book Is For This book is intended for data architects and developers with a basic level of knowledge of MongoDB. Familiarity with Pentaho is not expected. What You Will Learn Extract, load, and transform data from MongoDB collections to other datasources Design Pentaho Reports using different types of connections for MongoDB Create a OLAP mondrian schema for MongoDB Explore your MongoDB data using Pentaho Analyzer Utilize the drag and drop web interface to create dashboards Use Kettle Thin JDBC with MongoDB for analysis Integrate advanced dashboards with MondoDB using different types of connections Publish and run a report on Pentaho BI server using a web interface In Detail MongoDB is an open source, schemaless NoSQL database system. Pentaho as a famous open source Analysis tool provides high performance, high availability, and easy scalability for large sets of data. The variant features in Pentaho for MongoDB are designed to empower organizations to be more agile and scalable and also enables applications to have better flexibility, faster performance, and lower costs. Whether you are brand new to online learning or a seasoned expert, this book will provide you with the skills you need to create turnkey analytic solutions that deliver insight and drive value for your organization. The book will begin by taking you through Pentaho Data Integration and how it works with MongoDB. You will then be taken through the Kettle Thin JDBC Driver for enabling a Java application to interact with a database. This will be followed by exploration of a MongoDB collection using Pentaho Instant view and creating reports with MongoDB as a datasource using Pentaho Report Designer. The book will then teach you how to explore and visualize your data in Pentaho BI Server using Pentaho Analyzer. You will then learn how to create advanced dashboards with your data. The book concludes by highlighting contributions of the Pentaho Community. Style and approach A comprehensive, recipe-based guide to take complete advantage of the Pentaho Analytics for MongoDB.

Pentaho Data Integration Cookbook Second Edition is written in a cookbook format, presenting examples in the style of recipes.This allows you to go directly to your topic of interest, or follow topics throughout a chapter to gain a thorough in-depth knowledge.Pentaho Data Integration Cookbook Second Edition is designed for developers who are familiar with the basics of Kettle but who wish to move up to the next level.It is also aimed at advanced users that want to learn how to use the new features of PDI as well as and best practices for working with Kettle.

Create reports and solve common report problems with minimal fuss. About This Book Use this unique book to master the basics and advanced features of Pentaho 8 Reporting. A book showing developers and analysts with IT skills how to create and use the best possible reports using the Pentaho platform. Written with a very practical approach: full of tutorials and practical examples (source code included). Who This Book Is For This book is written for two types of professionals and students: Information Technologists with a basic knowledge of Databases and Java Developers with medium seniority. Developers will be interested to discover how to embed reports in a third-party Java application. What You Will Learn The basics of Pentaho Reporting (Designer and SDK) and its initial setup. Develop the most attractive reports on top of a wide range of data sources. Perform detailed customization of layout, parameterization, internationalization, behaviors, and more for your custom reports developed with Pentaho Reporting. Integrate Pentaho reports into third-party Java application with full control over interactions, layout, and behavior in general. Use Pentaho reports in the other components of the Pentaho Suite (BA Platform and PDI). In Detail This hands-on tutorial, filled with exercises and examples, introduces the reader to a variety of concepts within Pentaho Reporting. With screenshots that show you how reports look at design time as well as how they should look when rendered as PDF, Excel, HTML, Text, Rich-Text-File, XML, and CSV, this book also contains complete example source code that you can copy and paste into your environment to get up-and-running quickly. Updated to cover the features of Pentaho 8, this book will teach you everything you need to know to build fast, efficient reports using Pentaho. If your interest lies in the technical details of creating reports and you want to see how to solve common reporting problems with a minimum of fuss, this is the book for you. Style and approach A step-by-step guide covering technical topics relating to environments, best practices, and source code, to enable the reader to assemble the best reports and use them in existing Java applications.

This book has a perfect balance of concepts and their practical implementation along with solutions to make a highly available MongoDB server with clear instructions and guidance. If you are using MongoDB in a production environment and need a solution to make a highly available MongoDB server, this book is ideal for you. Familiarity with MongoDB is expected so that you understand the content of this book.

This textbook discusses the Problems in Big Data, Big Data Characteristics, Map Reduce Paradigm in Big Data, Various tools used in Big Data, examples of the application of Big Data and Analytics. Related courses / RPS)

Get to grips with key data visualization and predictive analytic skills using R About This Book Acquire predictive analytic skills using various tools of R Make predictions about future events by discovering valuable information from data using R Comprehensible guidelines that focus on predictive model design with real-world data Who This Book Is For If you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book. What You Will Learn Customize R by installing and loading new packages Explore the structure of data using clustering algorithms Turn unstructured text into ordered data, and acquire knowledge from the data Classify your observations using Naive Bayes, k-NN, and decision trees Reduce the dimensionality of your data using principal component analysis Discover association rules using Apriori Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression Use PMML to deploy the models generated in R In Detail R is statistical software that is used for data analysis. There are two main types of learning from data: unsupervised learning, where the structure of data is extracted automatically; and supervised learning, where a labeled part of the data is used to learn the relationship or scores in a target attribute. As important information is often hidden in a lot of data, R helps to extract that information with its many standard and cutting-edge statistical functions. This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naive Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages. Style and approach This is a practical book, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this book, but that can also be applied to any other data.

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes. Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.