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Foundation Book For Informatica Data Quality And Big Data Management
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Author :Daniel Lewis Publisher :Createspace Independent Publishing Platform ISBN 13 :9781981934010 Total Pages :104 pages Book Rating :4.9/5 (34 download)
Book Synopsis Foundation Book for Informatica Data Quality and Big Data Management by : Daniel Lewis
Download or read book Foundation Book for Informatica Data Quality and Big Data Management written by Daniel Lewis and published by Createspace Independent Publishing Platform. This book was released on 2017-07-05 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers end to end life cycle of building enterprise-class software in Informatica platform. This book covers Data Integration transformations, application deployment, execution, monitoring, parameterization and much more Purchasing this book does not entitle you for free Informatica software. You must have a license of Informatica software to use it.This book acts as a foundation for anyone who wants to learn Informatica Data Quality and Informatica Book Data. This book covers Model Repository, Data Integration Service and the Informatica Developer tool that form the crux of both Data Quality and Big Data Management products.
Book Synopsis Informatica Platform by : Keshav Vadrevu
Download or read book Informatica Platform written by Keshav Vadrevu and published by Createspace Independent Publishing Platform. This book was released on 2017-10-06 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Informatica Platform for beginners is the first ever book on Informatica's platform. This book acts as a foundation for anyone who wants to learn Informatica Data Quality and Informatica Book Data. This book covers Model Repository, Data Integration Service and the Informatica Developer tool that form the crux of both Data Quality and Big Data Management products. This book covers end to end life cycle of building enterprise-class software in Informatica platform. This book covers Data Integration transformations, application deployment, execution, monitoring, parameterization and much more NOTE: Purchasing this book does not entitle you for free Informatica software. You must have a license of Informatica software to use it. This book does not distribute software. Additional details are available at: http: //www.keshavvadrevu.com/books/informatica-platform.php
Book Synopsis Informatica Big Data Management by : Keshav Vadrevu
Download or read book Informatica Big Data Management written by Keshav Vadrevu and published by Createspace Independent Publishing Platform. This book was released on 2018-01-22 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches Informatica Big Data Management (BDM). Any existing Informatica Developers (PowerCenter or Informatica Platform) can leverage this book to learn BDM at a self-study peace. This book covers HDFS, Hive, Complex Files such as Avro, Parquet, JSON, & XML, BDM on Amazon AWS, BDM on Microsoft Azure ecosystems and much more. Spark execution mode including hierarchical data types and stateful variables are covered. This book covers DI on Big Data and does not cover data quality in BDM. Data Masking and Data Processor (B2B) on BDM are introduced and not covered in detail. NOTE: Purchasing this book does not entitle you for free software from Informatica. Readers should have a working Informatica BDM environment and a valid license key to execute the labs detailed within List of chapters and collateral downloads are available at Author's website: http: //keshavvadrevu.com/books/informatica-big-data-management
Download or read book Data Quality written by Richard Y. Wang and published by . This book was released on 2014-01-15 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Foundations of Data Quality Management by : Wenfei Fan
Download or read book Foundations of Data Quality Management written by Wenfei Fan and published by Springer Nature. This book was released on 2022-05-31 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Book Synopsis Data Architecture: A Primer for the Data Scientist by : W.H. Inmon
Download or read book Data Architecture: A Primer for the Data Scientist written by W.H. Inmon and published by Morgan Kaufmann. This book was released on 2014-11-26 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data
Book Synopsis Big Data by : Viktor Mayer-Schönberger
Download or read book Big Data written by Viktor Mayer-Schönberger and published by Houghton Mifflin Harcourt. This book was released on 2013 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.
Book Synopsis Principles of Big Data by : Jules J. Berman
Download or read book Principles of Big Data written by Jules J. Berman and published by Newnes. This book was released on 2013-05-20 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources
Book Synopsis Data Virtualization for Business Intelligence Systems by : Rick van der Lans
Download or read book Data Virtualization for Business Intelligence Systems written by Rick van der Lans and published by Elsevier. This book was released on 2012-07-25 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects.
Book Synopsis Foundations of Data Intensive Applications by : Supun Madhushanka Kamburugamuva Kamburugamuve Loku Acharilage
Download or read book Foundations of Data Intensive Applications written by Supun Madhushanka Kamburugamuva Kamburugamuve Loku Acharilage and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: PEEK "UNDER THE HOOD" OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems
Book Synopsis Informatica Power Center by : Keshav Vadrevu
Download or read book Informatica Power Center written by Keshav Vadrevu and published by CreateSpace. This book was released on 2014-06-18 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: PowerCenter - The Complete Reference is a one-stop guide for PowerCenter developers of all different levels: beginners, intermediate, advanced, expert an enterprise level. Step by step instructions with illustrations and about 100 screen shots guide you in learning every aspect of PowerCenter at your own pace. Start from the beginning or directly jump to a chapter to learn a specific aspect such as Web Services or XML. Learn PowerCenter or advance your PowerCenter skills at your own pace. Every part and chapter is uniquely designed around an aspect of the technology so that readers can pickup any specific chapter and learn it
Book Synopsis China’s e-Science Blue Book 2018 by : Chinese Academy of Sciences
Download or read book China’s e-Science Blue Book 2018 written by Chinese Academy of Sciences and published by Springer Nature. This book was released on 2019-11-19 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is jointly compiled by Chinese Academy of Sciences, Cyberspace Administration of China, Ministry of Education of the People’s Republic of China, Ministry of Science and Technology of the People’s Republic of China, Chinese Academy of Social Sciences, National Natural Science Foundation of China and Chinese Academy of Agricultural Sciences. Over the past several years, Chinese scholars have contributed numerous research works on the development of Chinese scientific information and technology, and produced a range of outstanding achievements. Focusing on the main topic of e-Science, this book explores the forefront of science and technology around the globe, the major demands in China and the main fields in China’s economic development. Furthermore, it reviews the major achievements and the typical cases in China's e-Science research. It provides a valuable reference source for future technological innovations and will introduce researchers and students in the area of e-Science to the latest results in China.
Download or read book Data Quality written by Thomas C. Redman and published by Random House Puzzles & Games. This book was released on 1992 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Quality begins with an explanation of what data is, how it is created and destroyed, then explores the true quality of data--accuracy, consistency and currentness. From there, the author covers the powerful methods of statistical quality control and process management to bear on the core processes that create, manipulate, use and store data values. Table of Contents: 1. Introduction; 2. Data and Information; 3. Dimensions of Data Quality; 4. Statistical Quality Control; 5. Process Management; 6. Process Representation and the Functions of Information Processing Approach; 7. Data Quality Requirements; 8. Measurement Systems and Data Quality; 9. Process Redesign Using Experimentation and Computer Simulation; 10. Managing Multiple Processes; 11. Perspective Prospects and Implications; 12. Summaries.
Book Synopsis Big Data Management by : Peter Ghavami
Download or read book Big Data Management written by Peter Ghavami and published by de Gruyter. This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world's leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
Download or read book Data Quality written by Rupa Mahanti and published by . This book was released on 2018 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Building a Data Integration Team by : Jarrett Goldfedder
Download or read book Building a Data Integration Team written by Jarrett Goldfedder and published by Apress. This book was released on 2020-02-27 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find the right people with the right skills. This book clarifies best practices for creating high-functioning data integration teams, enabling you to understand the skills and requirements, documents, and solutions for planning, designing, and monitoring both one-time migration and daily integration systems. The growth of data is exploding. With multiple sources of information constantly arriving across enterprise systems, combining these systems into a single, cohesive, and documentable unit has become more important than ever. But the approach toward integration is much different than in other software disciplines, requiring the ability to code, collaborate, and disentangle complex business rules into a scalable model. Data migrations and integrations can be complicated. In many cases, project teams save the actual migration for the last weekend of the project, and any issues can lead to missed deadlines or, at worst, corrupted data that needs to be reconciled post-deployment. This book details how to plan strategically to avoid these last-minute risks as well as how to build the right solutions for future integration projects. What You Will Learn Understand the “language” of integrations and how they relate in terms of priority and ownershipCreate valuable documents that lead your team from discovery to deploymentResearch the most important integration tools in the market todayMonitor your error logs and see how the output increases the cycle of continuous improvementMarket across the enterprise to provide valuable integration solutions Who This Book Is For The executive and integration team leaders who are building the corresponding practice. It is also for integration architects, developers, and business analysts who need additional familiarity with ETL tools, integration processes, and associated project deliverables.
Book Synopsis Executing Data Quality Projects by : Danette McGilvray
Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Academic Press. This book was released on 2021-05-27 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach Contains real examples from around the world, gleaned from the author’s consulting practice and from those who implemented based on her training courses and the earlier edition of the book Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
