Data warehousing Books

Book SynopsisCultural forces govern a synergistic relationship among information institutions that shapes their roles collectively and individually. The book addresses the origins of cultural information institutions, the history of the professions that run them, and the social imperative of information organization as a catalyst for semantic synergy.Table of ContentsChapter 1. Cultural Synergy and the Role of Information Institutions.- Chapter 2. The Nature of Information.- Chapter 3. What is (are) Information Studies?.- Chapter 4. The Synergistic Information Professions: Applications of the Information Process.- Chapter 5. Some History of Libraries, Library and Information Science, Information Technology.- Chapter 6. Gatekeepers: Information Dissemination.- Chapter 7. Knowledge Organization: Bibliography as Synergic Catalyst.- Chapter 8. Into the Future Boldly: The Imperative for Cultural Synergy.

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Book SynopsisYou want the rigour of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, theres some technical stuff in the middle, and it is absolutely essential -- but its not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an enterprise ontology. And Hans Hultgren similarly stresses the importance of the business concepts model. So its important. We get that. But: What on earth is an enterprise ontology/business concept model, cause I wont know if Ive got one if I dont know what Im looking for? If I cant find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault thats recommended? Its actually not as hard as some would fear to answer all of these questions, and its certainly worth the effort. This book just might save you a world of pain. Its a supplement to other material on Data Vault modeling, but its the vital missing link to finding simplicity for Data Vault success.

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Book SynopsisNearly 80 recipes to help you collect and transform data from multiple sources into a single data source, making it way easier to perform analytics on the dataKey Features Build data pipelines from scratch and find solutions to common data engineering problems Learn how to work with Azure Data Factory, Data Lake, Databricks, and Synapse Analytics Monitor and maintain your data engineering pipelines using Log Analytics, Azure Monitor, and Azure Purview Book DescriptionThe famous quote 'Data is the new oil' seems more true every day as the key to most organizations' long-term success lies in extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings to you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure.You'll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, SQL Serverless pools, Synapse integration pipelines, and Synapse data flows. You'll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you'll discover helpful tips on managing Azure SQL Database and learn about security, high availability, and performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview.By the end of this book, you'll be able to build superior data engineering pipelines along with having an invaluable go-to guide.What you will learn Process data using Azure Databricks and Azure Synapse Analytics Perform data transformation using Azure Synapse data flows Perform common administrative tasks in Azure SQL Database Build effective Synapse SQL pools which can be consumed by Power BI Monitor Synapse SQL and Spark pools using Log Analytics Track data lineage using Microsoft Purview integration with pipelines Who this book is forThis book is for data engineers, data architects, database administrators, and data professionals who want to get well versed with the Azure data services for building data pipelines. Basic understanding of cloud and data engineering concepts will help in getting the most out of this book.Table of ContentsTable of Contents Creating and Managing Data in Azure Data Lake Securing and Monitoring Data in Azure Data Lake Building Data Ingestion Pipelines Using Azure Data Factory Azure Data Factory Integration Runtime Configuring and Securing Azure SQL Database Implementing High Availability and Monitoring in Azure SQL Database Processing Data Using Azure Databricks Processing Data Using Azure Synapse Analytics Transforming Data Using Azure Synapse Dataflows Building the Serving Layer in Azure Synapse SQL Pool Monitoring Synapse SQL and Spark Pools Optimizing and Maintaining Synapse SQL and Spark Pools Monitoring and Maintaining Azure Data Engineering Pipelines

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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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Book SynopsisFormanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized. The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).Table of ContentsLearning.- From Theoretical Learnability to Statistical Measures of the Learnable.- ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis.- A “Top-Down and Prune” Induction Scheme for Constrained Decision Committees.- Mining Clusters with Association Rules.- Evolutionary Computation to Search for Strongly Correlated Variables in High-Dimensional Time-Series.- The Biases of Decision Tree Pruning Strategies.- Feature Selection as Retrospective Pruning in Hierarchical Clustering.- Discriminative Power of Input Features in a Fuzzy Model.- Learning Elements of Representations for Redescribing Robot Experiences.- “Seeing“ Objects in Spatial Datasets.- Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic.- Visualization.- Monitoring Human Information Processing via Intelligent Data Analysis of EEG Recordings.- Knowledge-Based Visualization to Support Spatial Data Mining.- Probabilistic Topic Maps: Navigating through Large Text Collections.- 3D Grand Tour for Multidimensional Data and Clusters.- Classification and Clustering.- A Decision Tree Algorithm for Ordinal Classification.- Discovering Dynamics Using Bayesian Clustering.- Integrating Declarative Knowledge in Hierarchical Clustering Tasks.- Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization.- Supervised Classification Problems: How to Be Both Judge and Jury.- Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification.- Exploiting Similarity for Supporting Data Analysis and Problem Solving.- Multiple Prototype Model for Fuzzy Clustering.- A Comparison of Genetic Programming Variants for Data Classification.- Fuzzy Clustering Based on Modified Distance Measures.- Building Classes in Object-Based Languages by Automatic Clustering.- Integration.- Adjusted Estimation for the Combination of Classifiers.- Data-Driven Theory Refinement Using KBDistAl.- Reasoning about Input-Output Modeling of Dynamical Systems.- Undoing Statistical Advice.- A Method for Temporal Knowledge Conversion.- Applications.- Intrusion Detection through Behavioral Data.- Bayesian Neural Network Learning for Prediction in the Australian Dairy Industry.- Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees.- Pump Failure Detection Using Support Vector Data Descriptions.- Data Mining for the Detection of Turning Points in Financial Time Series.- Computer-Assisted Classification of Legal Abstracts.- Sequential Control Logic Inferring Method from Observed Plant I/O Data.- Evaluating an Eye Screening Test.- Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure.- Media Mining.- Exploiting Structural Information for Text Classification on the WWW.- Multi-agent Web Information Retrieval: Neural Network Based Approach.- Adaptive Information Filtering Algorithms.- A Conceptual Graph Approach for Video Data Representation and Retrieval.

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Book SynopsisDieses Buch stellt Methoden der Begrifflichen Wissensverarbeitung vor und präsentiert Anwendungen aus unterschiedlichen Praxisfeldern. Im Methodenteil wird in moderne Techniken der Begrifflichen Datenanalyse und Wissensverarbeitung eingeführt. Hierbei werden die mathematischen Grundlagen abgehandelt und durch zahlreiche Beispiele anschaulich gemacht. Der zweite Teil des Buches richtet sich verstärkt an potentielle Anwender. An ausgewählten Anwendungen wird die Vorgehensweise bei der Datenanalyse und dem Information Retrieval mit den Methoden der Begrifflichen Wissensverarbeitung vorgestellt und ihr Potential aufgezeigt.Table of ContentsI: Methoden der Begrifflichen Wissensverarbeitung.- Begriffe und Implikationen.- ConImp - Ein Programm zur Formalen Begriffsanalyse.- Ähnlichkeit als Distanz in Begriffsverbänden.- Datenanalyse mit Fuzzy-Begriffen.- Terminologische Merkmalslogik in der Formalen Begriffsanalyse.- II: Anwendungen der Begrifflichen Wissensverarbeitung.- Formale Begriffsanalyse im Software Engineering.- Zugriffskontrolle bei Programmsystemen und im Datenschutz mittels Formaler Begriffsanalyse.- Inhaltliche Erschließung des Bereichs 'Sozialorientierte Gestaltung von Informationstechnik' - Ein begriffsanalytischer Ansatz.- Wissensdarstellungen in Informationssystemen, Fragetypen und Anforderungen an Retrievalkomponenten.- Ein TOSCANA-Erkundungssystem zur Literatursuche.- Ein Erkundungssystem zum Baurecht: Methoden der Entwicklung eines TOSCANA-Systems.- Begriffliche Erkundung semantischer Strukturen von Sprechaktverben.- Grundwerte, Ziele und Maßnahmen in einem regionalen Krankenhaus - Eine Anwendung des Verfahrens GABEK.- Normen- und regelgeleitete internationale Kooperationen - Formale Begriffsanalyse in der Politikwissenschaft.- Entwicklung eines kontextuellen Methodenkonzeptes mit Hilfe der Formalen Begriffsanalyse an Beispielen zum Risikoverständnis.- Über Möglichkeiten der Formalen Begriffsanalyse in der Mathematischen Archäochemie.

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Book SynopsisWith the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications.Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search.The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from www.search-computing.org.Trade ReviewFrom the reviews:“The book covers not only a wide range, but everything that is essential to the topic of Web information retrieval. … this book is an excellent starting point into the field of Web information retrieval, and can be recommended for classroom use.” (Gottfried Vossen, zbMATH, Vol. 1283, 2014)“... this book is a valuable resource for students and instructors in web IR, primarily as a reference to supplement course teaching. Researchers and practitioners should find the book a useful quick reference guide for key concepts, techniques, and recent trends in web IR.” (Wingyan Chung, ACM Computing Reviews, July 2014)Table of ContentsPart I Principles of Information Retrieval.- An Introduction to Information Retrieval.- The Information Retrieval Process.- Information Retrieval Models.- Classification and Clustering.- Natural Language Processing for Search.- Part II Information Retrieval for the Web.- Search Engines.- Link Analysis.- Recommendation and Diversification for the Web.- Advertising in Search.- Part III Advanced Aspects of Web Search.- Publishing Data on the Web.- Meta-Search and Multi-Domain Search.- Semantic Search.- Multimedia Search.- Search Process and Interfaces.- Human Computation and Crowd Search.

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Book SynopsisWith the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications.Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search.The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from www.search-computing.org.Trade ReviewFrom the reviews:“The book covers not only a wide range, but everything that is essential to the topic of Web information retrieval. … this book is an excellent starting point into the field of Web information retrieval, and can be recommended for classroom use.” (Gottfried Vossen, zbMATH, Vol. 1283, 2014)“... this book is a valuable resource for students and instructors in web IR, primarily as a reference to supplement course teaching. Researchers and practitioners should find the book a useful quick reference guide for key concepts, techniques, and recent trends in web IR.” (Wingyan Chung, ACM Computing Reviews, July 2014)Table of ContentsPart I Principles of Information Retrieval.- An Introduction to Information Retrieval.- The Information Retrieval Process.- Information Retrieval Models.- Classification and Clustering.- Natural Language Processing for Search.- Part II Information Retrieval for the Web.- Search Engines.- Link Analysis.- Recommendation and Diversification for the Web.- Advertising in Search.- Part III Advanced Aspects of Web Search.- Publishing Data on the Web.- Meta-Search and Multi-Domain Search.- Semantic Search.- Multimedia Search.- Search Process and Interfaces.- Human Computation and Crowd Search.

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