Information architecture Books

Book SynopsisThe Future of Enriched, Linked, Open and Filtered Metadata is a comprehensive and accessible guide to creating accurate, consistent, complete, user-centred and quality metadata that supports the user tasks of finding, identifying, selecting, obtaining and exploring information resources. Based on the author’s many years of academic research and work as a cataloguing and metadata librarian, it shows readers how they can configure, create, enhance and enrich their metadata for print and digital resources. The book applies examples using MARC21, RDA, FRBR, BIBFRAME, subject headings and name authorities. It also uses screenshots from cutting edge library management systems, discovery interfaces and metadata tools. Coverage includes: definitions, discussions, and comparisons among MARC, FRBR, LRM, RDA, Linked Data and BIBFRAME standards and models discussion of the underlying principles and protocols of Linked Data vis-à-vis library metadata practical metadata configuration, creation, management, and cases employing cutting edge LMS, discovery interfaces, formats and tools discussion around why metadata needs to be enriched, linked, open and filtered to ensure the information resources described are discoverable and user friendly consideration of metadata as a growing and continuously enhancing, customer-focused and user-driven practice where the aim is to support users to find and retrieve relevant resources for their research and learning. This practical book uses simple and accessible language to make sense of the many existing and emerging metadata standards, models and approaches. It will be a valuable resource for anyone involved in metadata creation, management and utilisation as well as a reference for LIS students, especially those undertaking information organisation, cataloguing and metadata modules.Trade Review"This work is a tour de force...Thoroughly recommended." -- Ian McCallum * Journal of the Australian Library and Information Association *Table of Contents Introduction to metadata Metadata strategies and quality indicators Metadata use cases Contemporary metadata principles Enriched and linked metadata Open metadata Filtered Metadata FRBR, LRM and the Notion of Work Resource Description and Access (RDA) BIBFRAME: a new metadata framework Crowdsourcing and user-generated metadata

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Book SynopsisWhy does one smoker die of lung cancer but another live to 100? The answer is 'The Hidden Half' - those random, unknowable variables that mess up our attempts to comprehend the world.We humans are very clever creatures - but we're idiots about how clever we really are. In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think. Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.Trade ReviewHighly original and challenging... Once you have read this book, you can't unread it. * Daniel Finkelstein, The Times *Fascinating and provocative. Blastland is one of the most original thinkers around. * Tim Harford - Financial Times columnist and author of The Undercover Economist *Elegantly written and mind-expanding, The Hidden Half will enthrall you with its storytelling while educating you with its scientific depth. * Daniel H. Pink - bestselling author of Drive *Brilliant. Blastland provides an explanation of the need for humility in the face of the inevitable limits to knowledge and our all-too-human temptation to tell stories about the world that go far beyond what the evidence will support. * Diane Coyle - Bennett Professor of Public Policy, Cambridge University *Fascinating... As John Wooden said, it's what you learn after you know it all that counts. * Andrew Gelman - author of Rich State Poor State Red State Blue State *Excellent. Blastland makes a compelling case that God is fond of playing dice with the cosmos-and the list of unpredictable things keeps growing, not shrinking. * Phillip Tetlock - bestselling author of Superforecasting *Beautifully written and often very funny. Anyone making decision that matter should enjoy this book and profit from its lessons. * Dame Frances Cairncross - Chair, Executive Committee of the Institute for Fiscal Studies *Thought-provoking. * UnHerd *Table of Contents0: Prologue 1: Bill is not Ben 2: I am not constant 3: Here is not there, now is not then 4: One path is not enough 5: The principle isn't practical 6: Big is not small 7: Big is not clear 8: The ignorant chicken 9: What to do 10: Postscript

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Book SynopsisTable of Contents1. An Introduction to Data Architecture2. The End-State Architecture - The "World Map"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations

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Book SynopsisSolution architecture is a relatively new specialism but is at the very heart of the relationship between business and IT. This book is an authoritative and practical introduction, suitable for new entrants to the field but also of benefit to experienced professionals wishing to consolidate their knowledge and skills. The tools and techniques of solution architecture are presented in the context of a framework and life cycle, taking a problem or idea through logical steps to design a holistic and evidence-based solution. There is a focus on collaboration with the business as well as other disciplines such as enterprise architecture, business analysis and cyber security.Trade ReviewThis book is an enjoyable and refreshing read, offering readers a comprehensive and contextual introduction to the discipline of solution architecture. As organisations are reimagining traditional business models, adopting agile ways of working and accelerating digital transformation agendas, this book highlights the importance of communication and collaboration throughout a solution architecture lifecycle including the voice of the customer and ongoing stakeholder interactions. This book provides a complementary framework for solution architecture that refreshingly re-enforces that ‘…unlike a strict methodology where activities are mandated, a framework is meant to be a guide, not a driver or constraint.' -- Chris Banks MBCS CITP, Director, Workplace Fusion LtdThis book provides a good overview of Solution Architecture Process and its alignment with Enterprise Architecture, -- Sachin Bansal, Enterprise / Lead Solution Architect, IBM Services, UK * *Note - Opinions expressed here are purely personal in individual capacity and do not reflect any endorsement by IBM or any other current/former employers *I recommend this book for those who want to grow their careers in Solution Architecture and for those considering related career roles. Complementing knowledge in specific technologies, this book can enable readers to grow from techniques and ways of thinking that can be applied for varying projects, delivery environments, and stakeholders. With a highly accessible style it will appeal to people with a range of backgrounds or career experiences. -- Mike Broomhead FBCS CITPAt last, a comprehensive study of a complex and often misunderstood subject. In addition to clarifying many aspects of Solution Architecture, the author has added personal insights based on his many years of experience. I particularly liked the sections where Solution Architecture is put into context with Business Architecture and other enterprise wide strategic domains. An excellent and enlightening book for all those interested in the topic of Solution Architecture. -- Paul Turner FBCS, BCS Author and Examiner'The book provides a good, clear, readable introduction to Solution Architecture. The topics are logically presented so that there is progressive style with concepts being illustrated through use of examples drawn from a realistic case study. There are also a good number of activities that the reader can work through. Reference is made to standards and methodologies without becoming fixated on them. It was very enjoyable and easy to read.' -- Dr Quentin Vaughan, Managing Client Partner, IBM Global Business ServicesTable of Contents Introduction to Solution Architecture Solution Architecture in the Context of Business and Enterprise Architecture A Framework for Solution Architecture Inputs to Solution Architecture Gap Analysis Stakeholder Interaction Solution Technology Definition Implementation

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Book SynopsisTrade Review"Among the metaverse of possible books on Text as Data that could have been published . . . I was pleased that my universe produced this one. I will assign this book as a critical part of my own course on content analysis for years to come, and it has already altered and improved the coherence of my own vocabulary and articulation for several critical choices underlying the process of turning text into data. . . . Highly recommend."---James Evans, Sociological Methods & Research

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Book SynopsisThis practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.

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Book SynopsisData Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest formUnderstand the matrix structure that your data should takeLearn basic concepts for testing the origin and validity of dataTransform your data into stable and usable numerical valuesUnderstand supervised and unsupe

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Book SynopsisMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.

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Book SynopsisGain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python What you will learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.Table of ContentsTable of Contents Module 1 Module 2

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Book SynopsisStreamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize data Key Features Acquire the skills necessary to perform analytics operations like an expert Discover hidden trends and insights in your data from various sources to make accurate predictions Reduce the time and effort required to derive insights from your data Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations. This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you’ll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you’ll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios. By the end of this book, you’ll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learn Speed up the cleansing, data preparing, and shaping process Perform operations and transformations on the data to suit your needs Blend different types of data sources for analysis Pivot and un-pivot the data for easy manipulation Perform aggregations and calculations on the data Encapsulate reusable logic into macros Develop high-quality, data-driven reports to improve consistency Who this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsTable of Contents Input data from files Working with databases Data Preparation Data Transformations Data Parsing Grouping Data Blending and Merging data Aggregations Dynamic Operations/ Tools Macros and Apps Downloads, APIs & Web Services Developer options Reporting with Alteryx Outputting Data

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Book SynopsisOffered as an introduction to the field of data structures andalgorithms, Open Data Structures covers the implementation andanalysis of data structures for sequences (lists), queues, priorityqueues, unordered dictionaries, ordered dictionaries, and graphs.Focusing on a mathematically rigorous approach that is fast, practical,and efficient, Morin clearly and briskly presents instruction alongwith source code. Analyzed and implemented in Java, the data structures presented inthe book include stacks, queues, deques, and lists implemented asarrays and linked-lists; space-efficient implementations of lists; skiplists; hash tables and hash codes; binary search trees includingtreaps, scapegoat trees, and red-black trees; integer searchingstructures including binary tries, x-fast tries, and y-fast tries;heaps, including implicit binary heaps and randomized meldable heaps;and graphs, including adjacency matrix and adjacency listrepresentations; and B-trees. A modern treatment of an essential computer science topic, OpenData Structures is a measured balance between classical topics andstate-of-the art structures that will serve the needs of allundergraduate students or self-directed learners.Table of ContentsAcknowledgments- xi Why This Book?- xiii 1. Introduction- 1 1.1 The Need for Efficiency- 2 1.2 Interfaces- 4 1.3 Mathematical Background- 9 1.4 The Model of Computation- 18 1.5 Correctness, Time Complexity, and Space Complexity- 19 1.6 Code Samples- 22 1.7 List of Data Structures- 22 1.8 Discussion and Exercises- 26 2. Array-Based Lists- 29 2.1 ArrayStack: Fast Stack Operations Using an Array- 30 2.2 FastArrayStack: An Optimized ArrayStack- 35 2.3 ArrayQueue: An Array-Based Queue- 36 2.4 ArrayDeque: Fast Deque Operations Using an Array- 40 2.5 DualArrayDeque: Building a Deque from Two Stacks- 43 2.6 RootishArrayStack: A Space-Efficient Array Stack- 49 2.7 Discussion and Exercises- 59 3. Linked Lists- 63 3.1 SLList: A Singly-Linked List- 63 3.2 DLList: A Doubly-Linked List- 67 3.3 SEList: A Space-Efficient Linked List- 71 3.4 Discussion and Exercises- 82 4. Skiplists- 87 4.1 The Basic Structure- 87 4.2 SkiplistSSet: An Efficient Sset- 90 4.3 SkiplistList: An Efficient Random-Access List- 93 4.4 Analysis of Skiplists- 98 4.5 Discussion and Exercises- 102 5. Hash Tables- 107 5.1 ChainedHashTable: Hashing with Chaining- 107 5.2 LinearHashTable: Linear Probing- 114 5.3 Hash Codes- 122 5.4 Discussion and Exercises- 128 6. Binary Trees- 133 6.1 BinaryTree: A Basic Binary Tree- 135 6.2 BinarySearchTree: An Unbalanced Binary Search Tree- 140 6.3 Discussion and Exercises- 147 7. Random Binary Search Trees- 153 7.1 Random Binary Search Trees- 153 7.2 Treap: A Randomized Binary Search Tree- 159 7.3 Discussion and Exercises- 168 8. Scapegoat Trees- 173 8.1 ScapegoatTree: A Binary Search Tree with Partial Rebuilding-173 8.2 Discussion and Exercises- 181 9. Red-Black Trees- 185 9.1 2-4 Trees- 186 9.2 RedBlackTree: A Simulated 2-4 Tree- 190 9.3 Summary- 205 9.4 Discussion and Exercises- 206 10. Heaps- 211 10.1 BinaryHeap: An Implicit Binary Tree- 211 10.2 MeldableHeap: A Randomized Meldable Heap- 217 10.3 Discussion and Exercises- 222 11. Sorting Algorithms- 225 11.1 Comparison-Based Sorting- 226 11.2 Counting Sort and Radix Sort- 238 11.3 Discussion and Exercises- 243 12. Graphs- 247 12.1 AdjacencyMatrix: Representing a Graph by a Matrix- 249 12.2 AdjacencyLists: A Graph as a Collection of Lists- 252 12.3 Graph Traversal- 256 12.4 Discussion and Exercises- 261 13. Data Structures for Integers- 265 13.1 BinaryTrie: A digital search tree- 266 13.2 XFastTrie: Searching in Doubly-Logarithmic Time- 272 13.3 YFastTrie: A Doubly-Logarithmic Time SSet- 275 13.4 Discussion and Exercises- 280 14. External Memory Searching- 283 14.1 The Block Store- 285 14.2 B-Trees- 285 14.3 Discussion and Exercises- 304 Bibliography- 309 Index- 317

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Book SynopsisThis is a book about Microsoft 365 Excel, or Excel 365. With a new formula calculations engine and many new built-in functions, creating formula solutions and business models in Excel 365 is dramatically easier than at any time in the history of spreadsheets. In addition, with the new data tools like Power Query, Power Pivot, and Power BI, performing data analysis to make data driven decisions can be easily done on data with different structures, with different sources, and on small and big data alike. With this exciting new Excel 365 version, we will learn three types for formulas: Worksheet, M Code, and DAX, and we will learn three types of Reporting/Dashboarding tools: Standard PivotTables, Data Model PivotTables, and Power BI Visualizations. This means that the New Excel 365 is the only app that matters in our age of analytics and data driven decisions. Who is this book/class for? Everyone. The book starts at the beginning and moves to an advanced level by telling a logical story about how to use Excel to solve calculation-based problems and answer crucial questions.

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Book SynopsisAlgorithms play a central role both in the theory and in the practice of computing. The goal of the authors was to write a textbook that would not trivialize the subject but would still be readable by most students on their own. The book contains over 120 exercises. Some of them are drills; others make important points about the material covered in the text or introduce new algorithms not covered there. The book also provides programming projects. From the Table of Contents: Chapter 1: Basic knowledge of Mathematics, Relations, Recurrence relation and Solution techniques, Function and Growth of functions. Chapter 2: Different Sorting Techniques and their analysis. Chapter 3: Greedy approach, Dynamic Programming, Branch and Bound techniques, Backtracking and Problems, Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, Binomial heap, B-Tree and Fibonacci Heap. Chapter 6: Approximation Algorithms, Sorting Networks, Matrix operations, Fast Fourier Transformation, Number theoretic Algorithm, Computational geometry Randomized Algorithms, String matching, NP-Hard, NP-Completeness, Cooks theorem.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisText in French: Si vous êtes á la tête d'une organisation ou si vous devez communiquer avec des dirigeants au sujet de la gestion des données, Sorienter dans le labyrinthe est votre guide. Les organisations qui veulent tirer de la valeur de leurs données doivent bien les gérer. Mais pour la plupart des cadres, la gestion des données semble obscure, compliquée et très technique. Vous n'avez pas le temps d'apprendre tous les détails ou d'aller au-delá du battage médiatique. S'orienter dans le labyrinthe vous aide á y parvenir. Basé sur les meilleures pratiques du Data Management Body of Knowledge (DMBOK2) de DAMA, il explique les principes fondamentaux et explique pourquoi ils sont importants. Il attire votre attention sur ce que vous devez savoir pour aider votre organisation á faire confiance á ses données et á en tirer de la valeur.

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Book SynopsisLeverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modeling Key Features Use mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud console Master optimization techniques for storage and query performance in BigQuery Engage with case studies on data exploration and preparation for advertising, transportation, and customer support data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learn Assess the quality of a dataset and learn best practices for data cleansing Prepare data for analysis, visualization, and machine learning Explore approaches to data visualization in BigQuery Apply acquired knowledge to real-life scenarios and design patterns Set up and organize BigQuery resources Use SQL and other tools to navigate datasets Implement best practices to query BigQuery datasets Gain proficiency in using data preparation tools, techniques, and strategies Who this book is forThis book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.Table of ContentsTable of Contents Introducing BigQuery and Its Components BigQuery Organization and Design Exploring Data in BigQuery Loading and Transforming Data Querying BigQuery Data Exploring Data with Notebooks Further Exploring and Visualizing Data An Overview of Data Preparation Tools Cleansing and Transforming Data Best Practices for Data Preparation, Optimization, and Cost Control Hands-On Exercise – Analyzing Advertising Data Hands-On Exercise Analyzing Transportation Data Hands-On Exercise – Analyzing Customer Support Data Summary and Future Directions

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Book SynopsisEverything 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 forIf 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.Table of ContentsTable of Contents A Brief History of Data Platforms Introducing Data Contracts How to Get Adoption in Your Organization Bringing Data Consumers and Generators Closer Together Embedding Data Governance What Makes Up a Data Contract A Contract-Driven Data Architecture A Sample Implementation Implementing Data Contracts In Your Organization Data Contracts in Practice

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