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

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool—a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse. Read more

This comprehensive guide to Microsoft Azure Data Engineering is designed to elevate your skills in data analytics, pipeline development, and Azure cloud solutions. Dive deep into essential concepts like data integration, ETL, SQL, Data Lakes, storage management, Data Governance, Data Strategy and big data architecture.This eBook is a Number #1 Best Seller in the following categories:#1 Data Warehousing#1 Computer Engineering#1 SQL ProgrammingIn today's job market, skilled Azure Data Engineers are rare, and their compensation reflects that rarity. They enjoy substantial salaries and the freedom to choose their ideal employers.As someone who has successfully become an Azure Data Engineer, I'm sharing the blueprint for my journey in this eBook.Inside, you'll find over 1,100 meticulously crafted questions and answers covering the core Azure components that Data Engineers rely on daily.This eBook is indispensable for:Rapidly enhancing your grasp of Azure Data Engineering technologyPreparing for an Azure Data Engineering interviewStudying for the DP-203 Microsoft Certification examServing as a reference guide for comprehending the operations of relevant Azure componentsSome of the key Data Engineering concepts covered include:Azure Platform: Azure Tenant, Azure Active Directory, Management Groups, Subscriptions, Resource GroupsDatabases: Azure Synapse Analytics, Azure SQL Database, SQL Server on Virtual Machines, Azure Cosmos DB, SQL Server On-Premises, DBA concepts, and SQL Performance TuningETL: Azure Data FactoryStorage: Azure Data Lake Gen2Real-Time Analytics: IoT Hub, Event Hub, Event Grid, Azure Stream Analytics, Data ExplorerData Science: Machine Learning, Databricks, Cognitive ServicesSecurity: Key Vault, RBAC, Private EndpointsDeployments: DevOps, ARM, and BICEP TemplatesProgramming: SQL, Python, PowerShell, and Azure CLIData Modeling: Kimball Methodology, Data WarehousingFront End: Function Apps, Logic AppsReporting: Power BI and Azure Analysis ServicesAzure Architecture: Lambda, Kappa, Data MeshMonitoring and Governance: Azure Purview, Azure Monitor, Log AnalyticsSecure your copy of "Microsoft Azure Data Engineering" today and embark on your journey to a rewarding six-figure career! Read more

Over 150 fishing destinations in Rocky Mountain National Park have been detailed with topographic maps, trail profiles, fishing tips, destination notes and photographs. The 256-page book contains 260 vivid full-color photographs of the fly fishing destinations in the Park. The book contains 40 pages of charts and tables containing detailed river and lake data, hatch charts and hiking trail information. The book also contains over 120 local expert and guide favorite fly patterns not found in other books. It is packed with information about every water in the Park containing fish. It is the most complete and thorough book ever written about fishing one of America's most popular National Parks and world-class fishing destinations. 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

This book provides an overview of Microsoft Fabric, an advanced analytics platform designed to modernize data estates. Fabric offers a comprehensive range of services for ingesting, storing, processing, and analyzing data within a unified environment. Its core, One Lake, centralizes data in a single location, enhancing governance, security, and collaboration among data teams. Fabric incorporates built-in governance mechanisms to restrict data access to authorized personnel only. It also promotes data democratization and facilitates analytics-driven decision-making. Its compatibility with various business tools empowers both novice and professional users to leverage data for informed decision-making. By eliminating data silos and enhancing accessibility, Fabric enables organizations to derive holistic insights and drive digital transformation efficiently. It serves as a bridge between business units and data professionals, catering to the needs of both while prioritizing data security stakeholders' requirements.We hope this book provides valuable insights into the capabilities and benefits of Microsoft Fabric Read more

Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads. The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform.What You Will LearnBuild dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data FactoryCreate data ingestion pipelines that integrate control tables for self-service ELTImplement a reusable logging framework that can be applied to multiple pipelinesIntegrate Azure Data Factory pipelines with a variety of Azure data sources and toolsTransform data with Mapping Data Flows in Azure Data FactoryApply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databasesDesign and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse AnalyticsGet started with a variety of Azure data services through hands-on examplesWho This Book Is ForData engineers and data architects who are interested in learning architectural and engineering best practices around ELT and ETL on the Azure Data Platform, those who are creating complex Azure data engineering projects and are searching for patterns of success, and aspiring cloud and data professionals involved in data engineering, data governance, continuous integration and deployment of DevOps practices, and advanced analytics who want a full understanding of the many different tools and technologies that Azure Data Platform provides Read more

Looking to become a certified Microsoft Azure Data Scientist Associate (DP-100)? This comprehensive eBook offers you 350 practice questions carefully crafted to help you ace the DP-100 certification exam while sharpening your real-world Azure data science skills.The DP-100 exam validates your expertise in applying data science and machine learning techniques using Azure’s cloud ecosystem. Whether you're just beginning your Azure data science journey or aiming to fine-tune your machine learning workflows, this book covers all essential topics — from data wrangling and visualization to model training, deployment, and automation on Azure.Key Topics Covered:Statistics : Build a solid foundation in probability, data distributions, and inference — the bedrock of meaningful machine learning outcomes.Machine Learning : Learn how to train predictive models, fine-tune algorithms, and uncover hidden patterns using Azure Machine Learning and Python.Data Mining : Master the techniques to extract actionable insights from large datasets, using both traditional and cloud-based tools.Data Visualization : Transform raw data into compelling charts and dashboards that drive data-driven decision-making and tell clear stories.Azure Data Lake : Explore the capabilities of Azure Data Lake for scalable and secure big data storage, designed for modern cloud workloads.Azure SQL Database : Understand how to integrate structured data into your models using Azure’s fully managed SQL cloud service for real-time analytics.Azure Synapse Analytics : Discover how to blend big data and enterprise warehousing to create seamless data pipelines and generate fast, intelligent insights.Azure Data Factory : Automate data ingestion, transformation, and movement using powerful workflows designed for efficiency and accuracy.Python : Use one of the world’s most popular programming languages to develop models, manipulate datasets, and automate workflows.Jupyter Notebooks : Combine code, narrative, and visuals in an interactive space perfect for data exploration, visualization, and collaborative model building.This eBook doesn’t just prepare you for Microsoft’s DP-100 exam — it equips you with the confidence and technical depth to work on real Azure-based machine learning projects. Whether you’re aspiring to become a cloud data scientist, machine learning engineer, or simply upgrading your skill set for today’s data-driven roles, these practice questions will guide your way.Prepare to build models, deploy them on Azure, and drive real business value from your data science expertise. The DP-100 certification is your gateway to one of the most in-demand career paths in cloud computing and AI — and this book will help you get there, one question at a time. Read more