Non-Invasive Data Governance Unleashed redefines how organizations approach data governance in the modern era and equips data leaders, data professionals, and anyone involved in data management with the tools and insights necessary to implement a data governance framework that is both effective and minimally disruptive. At the heart of Non-Invasive Data Governance (NIDG) and this book is the understanding that data governance is not just a technical or procedural task but fundamentally about people and their data-related behavior. The book unleashes rich and practical guidance on how to implement NIDG in various organizational contexts. Whether dealing with limited resources, decentralized data management, or the need to prepare data for AI applications, readers will find actionable steps and real-world examples that make the implementation process straightforward and achievable.Non-Invasive Data Governance Unleashed is a standout guide for aligning the elusive concept of data governance into an organization’s natural rhythm by minimizing overhead, respecting corporate culture, leveraging existing roles and responsibilities, and focusing on empowerment over enforcement. Replete with these and other unique and ingenious strategies, Seiner champions a proven approach that not only respects but elevates existing workflows to boost data’s potential and realized value. Data governance is no longer just a nice-to-have competency, so this book is a critical resource for any business and technology leader.Douglas B. LaneyData Analytics and AI strategy advisorBest-selling author of Infonomics and Data JuiceAs organizations accelerate their adoption of AI, the importance of foundational principles like data governance cannot be overstated. Bob’s new book provides a compelling blueprint for understanding why effective data governance is the bedrock of successful AI initiatives. By establishing trust, ensuring compliance, and fostering data quality, governance creates the environment where AI systems can thrive and deliver value. Bob clearly articulates how organizations that prioritize data governance unlock AI’s true potential while mitigating risks.David E. UlicneExecutive Director - Executive EducationCarnegie Mellon University’s Heinz CollegeBuilding on his groundbreaking series on Non-Invasive Data Governance, Seiner offers practical strategies to integrate governance seamlessly into daily operations without disruption.Dora BoussiasFounder, DoraB GlobalSeiner does it again! Non-Invasive Data Governance Unleashed isn’t just another data book—it’s a game-changer. Bob takes a methodical, no-nonsense approach to governance, showing how organizations can integrate it seamlessly into their operations without all the red tape.Chandra DonelsonAuthor of The Data DetectiveBob Seiner’s book on data governance is a step in the right direction for managing the modern IT environment. I recommend it highly.Bill Inmon“Father of the Data Warehouse”Everything in the data space, from architecture, to quality, to AI, and yes, even governance is really tough. Putting people front and center increases your chances.Thomas C. Redman, Ph. D.“the Data doc”Data Quality Solutions Read more

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: Gain a working understanding of several data architectures Learn the strengths and weaknesses of each approach Distinguish data architecture theory from reality Pick the best architecture for your use case Understand the differences between data warehouses and data lakes Learn common data architecture concepts to help you build better solutions Explore the historical evolution and characteristics of data architectures Learn essentials of running an architecture design session, team organization, and project success factors Free from product discussions, this book will serve as a timeless resource for years to come. Read more

Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copiesDelivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) processDelineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouseOffers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality Read more

Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing / business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. The book describes BEAM, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: Agile dimensional modeling using Business Event Analysis & Modeling (BEAM)Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun!Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how)Modeling by example not abstraction; using data story themes, not crow’s feet, to describe detailStoryboarding the data warehouse to discover conformed dimensions and plan iterative developmentVisual modeling: sketching timelines, charts and grids to model complex process measurement – simplyAgile design documentation: enhancing star schemas with BEAM dimensional shorthand notationSolving difficult DW/BI performance and usability problems with proven dimensional design patterns Read more

Dive into DuckDB and start processing gigabytes of data with ease—all with no data warehouse.DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you’ll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you’ll learn everything you need to get the most out of DuckDB—all through hands-on examples. Open up DuckDB in Action and learn how to:• Read and process data from CSV, JSON and Parquet sources both locally and remote • Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables • Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames • Prepare, ingest and query large datasets • Build cloud data pipelines • Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won’t need to read through pages of documentation—you’ll learn as you work. Get to grips with DuckDB's unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology DuckDB makes data analytics fast and fun! You don’t need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres. About the book DuckDB in Action guides you example-by-example from setup, through your first SQL query, to advanced topics like building data pipelines and embedding DuckDB as a local data store for a Streamlit web app. You’ll explore DuckDB’s handy SQL extensions, get to grips with aggregation, analysis, and data without persistence, and use Python to customize DuckDB. A hands-on project accompanies each new topic, so you can see DuckDB in action. What's inside• Prepare, ingest and query large datasets • Build cloud data pipelines • Extend DuckDB with custom functionality • Fast-paced SQL recap: From simple queries to advanced analytics About the reader For data pros comfortable with Python and CLI tools. About the author Mark Needham is a blogger and video creator at @?LearnDataWithMark. Michael Hunger leads product innovation for the Neo4j graph database. Michael Simons is a Java Champion, author, and Engineer at Neo4j. Read more

Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier’s techniques offer optimal value whether your projects involve “back-end” data management, “front-end” business analysis, or both.Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward successPart II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automationCollier brings together proven solutions you can apply right now—whether you’re an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results—and have fun along the way. Read more

Imagine transforming your organization's turbulent ocean of data into a clear, accurate, and strategically structured ecosystem. The book "Foundations of Ontology for Data Governance", the first volume of the innovative Ontology for Data Governance Collection, part of the renowned "Data Governance" collection, takes you on this fascinating journey.In this carefully crafted work by Prof. Marcão, you will find a bridge between ancient philosophy and the most advanced digital technologies, bringing to light the essential ontological engineering that underlies precise decisions and intelligent governance.Exploring fundamental concepts such as classes, instances, and relationships, the author guides you through a deep understanding of the semantic structure of data, revealing how the ontological triad can eliminate ambiguities and inconsistencies in your company. The clarity provided by ontologies is highlighted in practical comparisons with taxonomies and metadata, clarifying how each dimension contributes to efficient data governance.Key topics covered:• Fundamentals of ontology: classes, instances and relations.• Critical differences between ontologies, taxonomies, and metadata.• Relational, contextual and inferential expressiveness of ontologies.• Semantic interoperability and quality of information.• Modularity and standards for ontological modeling.• Ontologies and algorithmic ethics.• FAIR Principles and Methontology in action.By diving into the pages of this book, you will gain:• Clarity on the complexity of organizational data.• Advanced strategic decision-making capability based on structured data.• Tools to implement efficient governance and real interoperability.• Methods to ensure compliance, integrity, and strategic value of data.This book is an indispensable invitation to:• Managers and executives who seek strategic precision.• Data scientists and analysts looking for sophisticated and applicable techniques.• Academics interested in deepening their knowledge of ontological engineering.• IT professionals looking for practical solutions to real-world challenges.• All those interested in maximizing the strategic potential of data."Ontology Fundamentals for Data Governance" is not just a read, but a profound transformation in how you perceive and utilize data in your organization. Get ready to elevate your strategic capacity, deeply understand governance dynamics, and ensure a more competitive, ethical, and innovative future.Take this essential step towards intelligent data governance now!About the author.Prof. Marcão, Master in Information Technology and author of more than 200 books, is a technology thinker who has been working in information architecture and attribute engineering since the 1980s. His most significant contribution to the field lies in the development of innovative methodologies for Data Governance, Big Data, and Data Warehouse, including data modeling patterns, data warehouse, data model validation and management, as well as data mart development. Read more

Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligenceBegins with fundamental design recommendations and progresses through increasingly complex scenariosPresents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and moreDraws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and moreDesign dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition. Read more

A step-by-step guide to harness the power of Microsoft Fabric in developing data analytics solutions for various use casesKey FeaturesExplore Microsoft Fabric and its features with real-world examplesBuild data analytic solutions for lakehouse, data warehouse, real-time analytics, and data scienceMonitor, manage, and administer your Fabric platform and analytics system for flexibility, performance, security, and controlBook DescriptionMicrosoft Fabric is the first and only service/product in the market to offer a unified and comprehensive solution designed for the AI era. It seamlessly brings together data integration, OneLake, transformation, visualization, universal security, and a unified business model.Learn Microsoft Fabric provides an overview of Microsoft Fabric, its components, and the wider analytics landscape. You'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll then learn to build end-to-end lakehouse and data warehouse solutions using the medallion architecture. You’ll unlock the potential of real-time analytics and implement machine learning and AI models. As you move ahead, you’ll learn how to monitor workloads, and administer Fabric across tenants, capacities, and workspaces. You’ll enhance security and governance practices in Microsoft Fabric. You’ll implement CI/CD workflows with Azure DevOps or GitHub. In the end, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey.By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and essential concepts and principles of Microsoft Fabric.What you will learnExplore Microsoft Fabric and the different services it providesBuild end-to-end data analytics solution to scale and manage high performanceIntegrate data from different types of data sourcesApply transformation with Spark, Notebook, and T-SQLUnderstand, learn, and implement real-time stream processing and data science capabilitiesPerform end-to-end processes for building data analytics solutions in the AI eraDrive insights by leveraging Power BI for reporting and visualizationImprove productivity with AI assistance and Copilot integrationWho This Book Is ForThis book is intended for data professionals, such as data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers, who want to build a future-ready data analytics solution for the era of AI in driving their organization for long-term success. For PySpark and SQL students getting into the data analytics domain, the book offers a broad foundation to develop skills to build end-to-end analytics systems for different use cases. Basic knowledge of SQL and Spark is presumed.Table of ContentsOverview of Microsoft Fabric and Understanding Different ConceptsUnderstanding Different Workloads in Microsoft Fabric and Getting StartedLakehouse (Medallion Architecture) and Data MeshData WarehouseReal-time Analytics CapabilitiesExploring Data Science CapabilitiesMonitoring overview and monitoring different workloadsAdministering FabricSecurity and Governance overviewContinuous Integration and Continuous Deployment (CI/CD)Overview - AI Assisted with Co-pilot integration Read more

How To Skyrocket Your Career & Life By Mastering SQL Without Spending Thousands Of Dollars, Even If You Don't Have A Coding Background…Data is the New Oil!And in this digital age, the data and data management industry is massively growing.According to Expert Market Research, In the Year 2022, the estimated value of the data management industry is 92.99 billion dollars. (That's HUGE!) But the best part is it is expected to grow by over 150 billion dollars by 2027.This skill is the foundational skill to learn to get the most out of the High-Paying and hottest jobs in the market, like data scientists, Data architects, Data Analysts, BI developers, Data warehouse developers, Database Administrators, etc.In addition, numerous high-paying freelancers enjoy the freedom and traveling the world by mastering this data skill.In Simple words, Learning SQL can open doors for countless valuable opportunities.SQL is everywhere.Almost all the GIANT TECH companies like Meta, Google, Netflix, Uber, Airbnb, Amazon … and the list is endless, use SQL for their super-efficient database systems.SQL has been and will continue to be the KING of DATA LANGUAGES."Fortunately, Now is an unbelievable opportunity to establish a solid position in this growing industry."Here's just a fraction of what you're going to explore in the book:A proven 3-module learning program to 3X your learning speed.Step-by-step installation of SQL server in windows, mac, and Linux OS so you can quickly put your hands on the cool stuff.Mistakes that even experienced developers make. Avoid these mistakes and shine at your workplace.Revealed: A simple mindset hack that can help you to master any programming language quickly.All the essential concepts frequently asked in interviews ( Your unfair advantage over other candidates).Beautiful graphics and illustrations to cement the concepts in your mind and make things interesting and not boring.Carefully selected Exercises and concrete examples for your progressive learning. (no stones unturned)Every single technical term is clearly explained in this book.A unique and much better way to understand SQL Joins that only the top 1% of developers know and share.Advanced topics like Functions, triggers, indexes, and stored procedures are explained with simple, easy-to-follow examples and exercises ( for the reader's maximum lesson retention ).And much, much more!Plus, a mini-SQL pocketbook and datasets to practice SQL are included in this book.Learning SQL seems quite daunting at first, especially to people with no prior programming background. But with this easy-to-follow book, even a grandma with no computer skills can learn SQL with a liberating experience.Even If you get stuck or overwhelmed, you can always reach out over email or make a quick post in our private supporting developer's community on social media ( provided inside the book) – we're always happy to help you with any questions or doubts you might have.This book is not another 500-page dense theory-packed boring textbook filled with repetitive technical jargon and fluff, which you need a computer science degree to understand. This is not just a textbook; this is an interactive crash-course workbook! All this premium knowledge, along with all the additional bonuses, is available for you – without spending thousands of dollars to learn the same thing.If you want to kickstart your SQL learning today, then scroll up and click on "add to cart." Read more

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now!Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.Master these ten objectives:Build an unstructured data warehouse using the 11-step approachIntegrate text and describe it in terms of homogeneity, relevance, medium, volume, and structureOvercome challenges including blather, the Tower of Babel, and lack of natural relationshipsAvoid the Data Junkyard and combat the Spider's WebReuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative developmentApply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacementDesign the Document Inventory system and link unstructured text to structured dataLeverage indexes for efficient text analysis and taxonomies for useful external categorizationManage large volumes of data using advanced techniques such as backward pointersEvaluate technology choices suitable for unstructured data processing, such as data warehouse appliancesThe following outline briefly describes each chapter's content:Chapter 1 defines unstructured data and explains why text is the main focus of this book.Chapter 2 addresses the challenges one faces when managing unstructured data.Chapter 3 discusses the DW 2.0 architecture, which leads into the role of the unstructured data warehouse. The unstructured data warehouse is defined and benefits are given. There are several features of the conventional data warehouse that can be leveraged for the unstructured data warehouse, including ETL processing, textual integration, and iterative development. Chapter 4 focuses on the heart of the unstructured data warehouse: Textual Extract, Transform, and Load (ETL).Chapter 5 describes the 11 steps required to develop the unstructured data warehouse.Chapter 6 describes how to inventory documents for maximum analysis value, as well as link the unstructured text to structured data for even greater value.Chapter 7 goes through each of the different types of indexes necessary to make text analysis efficient. Indexes range from simple indexes, which are fast to create and are good if the analyst really knows what needs to be analyzed before the indexing process begins, to complex combined indexes, which can be made up of any and all of the other kinds of indexes.Chapter 8 explains taxonomies and how they can be used within the unstructured data warehouse.Chapter 9 explains ways of coping with large amounts of unstructured data. Techniques such as keeping the unstructured data at its source and using backward pointers are discussed. The chapter explains why iterative development is so important.Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. In addition, the data warehouse appliance is discussed.Chapters 11, 12, and 13 put all of the previously discussed techniques and approaches in context through three case studies. Read more

Si es verdad que el 75% de los activos de las empresas Standard & Poor 's no son físicos: ¿De qué estamos hablando? ¡De Datos! Qué sería de empresas como Booking, AirBNB, Facebook sin sus datos (y los nuestros) esto es motivo más que suficiente para entender la importancia del Data Management.Hubo un tiempo en que dijimos a las empresas que tenían que mirar sus datos: nació el business intelligence. Hubo un tiempo en el que quisimos democratizar el uso de los datos y nació el data-driven. Pasado un tiempo, comprendimos que gracias a la virtualización y la nube no es necesario mover los datos y nació Data Virtualization. Algunos me dicen que en el fondo nada ha cambiado. Tenemos una tecnología impresionante, el cloud y el cómputo en la nube disparan el potencial de todo, pero también disparan la entropía.No estamos en la “transformación digital”, no se trata de algo que llega y al que hay que adaptarse, se trata de hablar de “hábito de evolución digital”. Porque se trata de un proceso continuo no de algo puntual. Un proceso que necesita la Data Governance de la misma forma que necesita otros aspectos relacionados con los datos (Data Quality, Data Analyzing, Data Virtualization, etc.)Estamos en la era de los Metadatos. Si es verdad que la Inteligencia de Negocio ha cristalizado la estrategia (pasando de “qué” hacer a “como” hacerlo), la Data Virtualization ha permitido liberar los datos de vínculos físicos; la Data Governance va a focalizar sus esfuerzos en los metadatos. Ya no importa la cantidad de datos que podamos tratar ni como lo tratamos. Necesitamos saber que estos datos nos dicen y quien decide que digan algo. Sin Data Governance no existe el Data Management, es la visión de DAMA y la comparto completamente.Desde hace unos cuantos años me dedico a la gestión de datos, aunque existe un framework referente super importante como el DmBok 2 de DAMA que utilizo a diario, me pareció interesante desarrollar algo muy didáctico para todos aquellos que se acercan al mundo del Data Management. Podríamos decir que este libro es una especie de diccionario alargado para comprender mejor este mundo tan fascinante de los datos.¡Ahora más que nunca el dato no es una opción es el negocio!Hablaremos de: Data Literacy, Metadatos, Data Governance, Business Glossary, Data Dictionary, Data Catalog, ETL, ELT, Master Data, Data Lake, Data Warehouse,OLAP, ROLAP, MOLAP, DOLAP y HOLAP, Data Fabric, Data Mesh, Enfoque Declarativo vs Enfoque Procedural, Data Vault, Data Monetization, CDC, Data Virtualization y DmBoK2 Read more

Enterprises are experimenting with using Hadoop to build Big Data Lakes, but many projects are stalling or failing because the approaches that worked at Internet companies have to be adopted for the enterprise. This practical handbook guides managers and IT professionals from the initial research and decision-making process through planning, choosing products, and implementing, maintaining, and governing the modern data lake.You'll explore various approaches to starting and growing a Data Lake, including Data Warehouse off-loading, analytical sandboxes, and "Data Puddles." Author Alex Gorelik shows you methods for setting up different tiers of data, from raw untreated landing areas to carefully managed and summarized data. You'll learn how to enable self-service to help users find, understand, and provision data; how to provide different interfaces to users with different skill levels; and how to do all of that in compliance with enterprise data governance policies. Read more

The industry’s most complete, useful, and up-to-date guide to SQL Server 2014.You’ll find start-to-finish coverage of SQL Server’s core database server and management capabilities: all the real-world information, tips, guidelines, and examples you’ll need to install, monitor, maintain, and optimize themost complex database environments. The provided examples and sample code provide plenty of hands-on opportunities to learn more about SQL Server and create your own viable solutions.Four leading SQL Server experts present deep practical insights for administering SQL Server, analyzing and optimizing queries, implementing data warehouses, ensuring high availability, tuning performance, and much more. You will benefit from their behind-the-scenes look into SQL Server, showing what goes on behind the various wizards and GUI-based tools. You’ll learn how to use the underlying SQL commands to fully unlock the power and capabilities of SQL Server.Writing for all intermediate-to-advanced-level SQL Server professionals, the authors draw on immense production experience with SQL Server. Throughout, they focus on successfully applying SQL Server 2014’s most powerful capabilities and its newest tools and features.Detailed information on how to… Understand SQL Server 2014’s new features and each edition’s capabilities and licensing Install, upgrade to, and configure SQL Server 2014 for better performance and easier management Streamline and automate key administration tasks with Smart Admin Leverage powerful new backup/restore options: flexible backup to URL, Managed Backup to Windows Azure, and encrypted backups Strengthen security with new features for enforcing “least privilege” Improve performance with updateable columnstore indexes, Delayed Durability, and other enhancements Execute queries and business logic more efficiently with memoryoptimized tables, buffer pool extension, and natively-compiled stored procedures Control workloads and Disk I/O with the Resource Governor Deploy AlwaysOn Availability Groups and Failover Cluster Instances to achieve enterprise-class availability and disaster recovery Apply new Business Intelligence improvements in Master Data Services, data quality, and Parallel Data Warehouse Read more

With our appetites for data on the rise, it has become more important than ever to use UML (Unified Modeling Language) to capture and precisely represent all of these data requirements. Learn how to construct UML data models by working through a series of exercises and self-assessment tests. Beginners can learn the UML directly. Experienced modelers can leverage their understanding of existing database notations, as the book extensively compares the UML to traditional data modeling (Information Engineering). Discover a new way of representing data requirements and communicating better with your business customers. Understand what UML constructs mean and how to properly use them. Learn subtleties of the UML. Become a power UML developer. Practice constructing data models with the exercises. The back of the book answers every exercise. Assess your mastery of the material. Each part has a multiple-choice test that can quantify your understanding. Improve your ability to abstract - think about different ways of representation - as you construct data models. Measure the quality of your data models. Be able to create database designs (DDL code) starting from a UML data model. Be able to write SQL database queries using a data model as a blueprint. Know the differences among operational models, data warehouse models, enterprise models, and master models. They are all aspects of data modeling. This book is concise and to the point. You will learn by induction through reading, practice, and feedback. Read more

Master the intricacies of Snowflake and prepare for the SnowPro Advanced Architect Certification exam with this comprehensive study companion. This book provides robust and effective study tools to help you prepare for the exam and is also designed for those who are interested in learning the advanced features of Snowflake. The practical examples and in-depth background on theory in this book help you unleash the power of Snowflake in building a high-performance system. The best practices demonstrated in the book help you use Snowflake more powerfully and effectively as a data warehousing and analytics platform.Reading this book and reviewing the concepts will help you gain the knowledge you need to take the exam. The book guides you through a study of the different domains covered on the exam: Accounts and Security, Snowflake Architecture, Data Engineering, and Performance Optimization. You’ll also be well positioned to apply your newly acquired practical skills to real-world Snowflake solutions. You will have a deep understanding of Snowflake to help you take full advantage of Snowflake’s architecture to deliver value analytics insight to your business.What You Will LearnGain the knowledge you need to prepare for the examReview in-depth theory on Snowflake to help you build high-performance systemsBroaden your skills as a data warehouse designer to cover the Snowflake ecosystemOptimize performance and costs associated with your use of the Snowflake data platformShare data securely both inside your organization and with external partnersApply your practical skills to real-world Snowflake solutionsWho This Book Is ForAnyone who is planning to take the SnowPro Advanced Architect Certification exam, those who want to move beyond traditional database technologies and build their skills to design and architect solutions using Snowflake services, and veteran database professionals seeking an on-the-job reference to understand one of the newest and fastest-growing technologies in data Read more

Three books by the bestselling authors on Data Warehousing! The most authoritative guides from the inventor of the technique all for a value price.The Data Warehouse Toolkit, 3rd Edition (9781118530801) Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 10 years, Kimball has improved on his earlier techniques and created many new ones. In this 3rd edition, he will provide a comprehensive collection of all of these techniques, from basic to advanced.The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775) Complete coverage of best practices from data warehouse project inception through on-going program management. Updates industry best practices to be in sync with current recommendations of Kimball Group. Streamlines the lifecycle methodology to be more efficient and user-friendlyThe Data Warehouse ETL Toolkit (9780764567575) shows data warehouse developers how to effectively manage the ETL (Extract, Transform, Load) phase of the data warehouse development lifecycle. The authors show developers the best methods for extracting data from scattered sources throughout the enterprise, removing obsolete, redundant, and inaccurate data, transforming the remaining data into correctly formatted data structures, and then physically loading them into the data warehouse.This book provides complete coverage of proven, time-saving ETL techniques. It begins with a quick overview of ETL fundamentals and the role of the ETL development team. It then quickly moves into an overview of the ETL data structures, both relational and dimensional. The authors show how to build useful dimensional structures, providing practical examples of beginning through advanced techniques. Read more