Download or read book Driving Data Quality with Data Contracts written by Andrew Jones and published by Packt Publishing Ltd. This book was released on 2023-06-30 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business value Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand data contracts and their power to resolving the problems in contemporary data platforms Learn how to design and implement a cutting-edge data platform powered by data contracts Access practical guidance from the pioneer of data contracts to get expert insights on effective utilization Book DescriptionDespite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.What you will learn Gain insights into the intricacies and shortcomings of today's data architectures Understand exactly how data contracts can solve prevalent data challenges Drive a fundamental transformation of your data culture by implementing data contracts Discover what goes into a data contract and why it's important Design a modern data architecture that leverages the power of data contracts Explore sample implementations to get practical knowledge of using data contracts Embrace best practices for the successful deployment of data contracts Who this book is for If you’re a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.
Download or read book Data Quality in the Age of AI written by Andrew Jones and published by Packt Publishing Ltd. This book was released on 2024-05-24 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric culture Key Features Gain a profound understanding of the interplay between data quality and AI Explore strategies to improve data quality with practical implementation and real-world results Acquire the skills to measure and evaluate data quality, empowering data-driven decisions Purchase of the Kindle book includes a free PDF eBook Book DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learn Discover actionable steps to establish data quality as the foundation of your data culture Enhance data quality directly at its source with effective strategies and best practices Elevate data quality standards and enhance data literacy within your organization Identify and measure data quality within the dataset Adopt a product mindset to address data quality challenges Explore emerging architectural patterns like data mesh and data contracts Assign roles, responsibilities, and incentives for data generators Gain insights from real-world case studies Who this book is for This report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Download or read book Managing Data Integrity for Finance written by Jane Sarah Lat and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Level up your career by learning best practices for managing the data quality and integrity of your financial data Key Features Accelerate data integrity management using artificial intelligence-powered solutions Learn how business intelligence tools, ledger databases, and database locks solve data integrity issues Find out how to detect fraudulent transactions affecting financial report integrity Book DescriptionData integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations. The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases. After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What you will learn Develop a customized financial data quality scorecard Utilize business intelligence tools to detect, manage, and resolve data integrity issues Find out how to use managed cloud-based ledger databases for financial data integrity Apply database locking techniques to prevent transaction integrity issues involving finance data Discover the methods to detect fraudulent transactions affecting financial report integrity Use artificial intelligence-powered solutions to resolve various data integrity issues and challenges Who this book is for This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it’ll help you grasp the more advanced topics covered in this book.
Download or read book Data Engineering Best Practices written by Richard J. Schiller and published by Packt Publishing Ltd. This book was released on 2024-10-11 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
Download or read book The Definitive Guide to Data Integration written by Pierre-Yves BONNEFOY and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape.
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
Download or read book Implementing Data Mesh written by Jean-Georges Perrin and published by "O'Reilly Media, Inc.". This book was released on 2024-10-15 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data Mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code. You'll explore a simple and intuitive process for identifying key data mesh components and data products, and learn about a consistent set of interfaces and access methods that make data products easy to consume. This approach ensures that your data products are easily accessible and the data mesh ecosystem is easy to navigate. With this book, you'll learn how to: Identify, define, and build data products that interoperate within an enterprise data mesh Build a data mesh fabric that binds data products together Build and deploy data products in a data mesh Establish the organizational structure to operate data products, data platforms, and data fabric Learn an innovative architecture that brings data products and data fabric together into the data mesh About the authors: Jean-Georges "JG" Perrin is a technology leader focusing on building innovative and modern data platforms. Eric Broda is a technology executive, practitioner, and founder of a boutique consulting firm that helps global enterprises realize value from data.
Download or read book Data Products and the Data Mesh written by Alberto Artasanchez and published by The Data Science Ninja. This book was released on with total page 643 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Data Products and the Data Mesh" is a comprehensive guide that explores the emerging paradigm of the data mesh and its implications for organizations navigating the data-driven landscape. This book equips readers with the knowledge and insights needed to design, build, and manage effective data products within the data mesh framework. The book starts by introducing the core concepts and principles of the data mesh, highlighting the shift from centralized data architectures to decentralized, domain-oriented approaches. It delves into the key components of the data mesh, including federated data governance, data marketplaces, data virtualization, and adaptive data products. Each chapter provides in-depth analysis, practical strategies, and real-world examples to illustrate the application of these concepts. Readers will gain a deep understanding of how the data mesh fosters a culture of data ownership, collaboration, and innovation. They will explore the role of modern data architectures, such as data marketplaces, in facilitating decentralized data sharing, access, and monetization. The book also delves into the significance of emerging technologies like blockchain, AI, and machine learning in enhancing data integrity, security, and value creation. Throughout the book, readers will discover practical insights and best practices to overcome challenges related to data governance, scalability, privacy, and compliance. They will learn how to optimize data workflows, leverage domain-driven design principles, and harness the power of data virtualization to drive meaningful insights and create impactful data products. "Data Products and the Data Mesh" is an essential resource for data professionals, architects, and leaders seeking to navigate the complex world of data products within the data mesh paradigm. It provides a comprehensive roadmap for building a scalable, decentralized, and innovative data ecosystem that empowers organizations to unlock the full potential of their data assets and drive data-driven success.
Download or read book Data Quality written by Yng-Yuh Richard Wang and published by Springer Science & Business Media. This book was released on 2001 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.
Download or read book Data Quality Fundamentals written by Barr Moses and published by "O'Reilly Media, Inc.". This book was released on 2022-09 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets
Download or read book Handbook of Data Quality written by Shazia Sadiq and published by Springer Science & Business Media. This book was released on 2013-08-13 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.
Download or read book Data Quality written by Thomas C. Redman and published by Digital Press. This book was released on 2001 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality. Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it. The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points. Allows readers to go directly to the topic of interest Provides web-based material so readers can cut and paste figures and tables into documents within their organizations Gives step-by-step instructions for applying most techniques and summarizes what "works"
Download or read book The Practitioner s Guide to Data Quality Improvement written by David Loshin and published by Elsevier. This book was released on 2010-11-22 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Download or read book Blockchain Big Data and Machine Learning written by Neeraj Kumar and published by CRC Press. This book was released on 2020-09-24 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book: Converges Blockchain, Big Data and Machine learning in one volume. Connects Blockchain technologies with the data centric applications such Big data and E-Health. Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries. Covers big data analytics examples using R. Includes lllustrative examples in python for blockchain creation.
Download or read book Data Management at Scale written by Piethein Strengholt and published by O'Reilly Media. This book was released on 2020-07-29 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Download or read book Theory and Practice of Business Intelligence in Healthcare written by Khuntia, Jiban and published by IGI Global. This book was released on 2019-12-27 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration.
