Advertisement
| dp 203 data engineering: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| dp 203 data engineering: Azure Data Engineer Associate Certification Guide - Second Edition GIACINTO. METTAPALLI PALMIERI (SURENDRA. ALEX, NEWTON.), Surendra Mettapalli, Newton Alex, 2024-05-23 Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF Key Features - Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources - Gain up-to-date skills to thrive in the dynamic world of cloud data engineering - Build secure and sustainable data solutions using Azure services Book Description One of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you'll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer. What you will learn - Design and implement data lake solutions with batch and stream pipelines - Secure data with masking, encryption, RBAC, and ACLs - Perform standard extract, transform, and load (ETL) and analytics operations - Implement different table geometries in Azure Synapse Analytics - Write Spark code, design ADF pipelines, and handle batch and stream data - Use Azure Databricks or Synapse Spark for data processing using Notebooks - Leverage Synapse Analytics and Purview for comprehensive data exploration - Confidently manage VMs, VNETS, App Services, and more Who this book is for This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book. A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered. |
| dp 203 data engineering: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| dp 203 data engineering: DP 203: Data Engineering on Microsoft Azure : Study Guide with Practice Questions and Labs - Volume 2 of 2 :Design, Monitor, and Optimize Data Processing Solutions with Security - First Edition - 2021 I. P. Specialist, NOUMAN AHMED KHAN., 2021-10-15 DP -203: Data Engineering on Microsoft Azure: Study Guide with Practice Questions and Labs - First Edition About the Author Nouman Ahmed Khan: AWS/Azure/GCP-Architect, CCDE, CCIEx5 (R&S, SP, Security, DC, Wireless), CISSP, CISA, CISM, CRISC, ISO27K-LA is a Solution Architect working with a global telecommunication provider. He works with enterprises, mega-projects, and service providers to help them select the best-fit technology solutions. He also works as a consultant to understand customer business processes and helps select an appropriate technology strategy to support business goals. He has more than fifteen years of experience working with global clients. PASS THE DP-203 Microsoft Azure Data Engineer Associate EXAM With Confidence in just 4 Weeks!. Are you looking to learn about the foundational and some advanced knowledge of core data concepts and how they are implemented using Microsoft Azure data services? This book is an ideal resource to start your journey with confidence.No prior experience in the cloud is required. This is a highly practical, intensive, yet comprehensive book that will teach you to become an Azure Data Engineer. It's a perfect resource to pass the Microsoft Azure Data Engineer Associate exam on the first attempt. The book Includes: - Covers complete exam blueprint - Practice Questions. - Mind-maps - Hand-on practice labs. - Real-world examples. - Exam tips. Topics Covered: - Design and implement Data Storage - Design and develop Data Processing - Design and implement Data Security - Monitor and optimize Data Storage and Data Processing |
| dp 203 data engineering: Microsoft Certified Exam guide - Azure Data Engineer Associate (DP-203) Cybellium Ltd, Unlock the Power of Data with Azure Data Engineering! Are you ready to become a Microsoft Azure Data Engineer Associate and harness the transformative potential of data in the cloud? Look no further than the Microsoft Certified Exam Guide - Azure Data Engineer Associate (DP-203). This comprehensive book is your ultimate companion on the journey to mastering Azure data engineering and acing the DP-203 exam. In today's data-driven world, organizations depend on the efficient management, processing, and analysis of data to make critical decisions and drive innovation. Microsoft Azure provides a cutting-edge platform for data engineers to design and implement data solutions, and the demand for skilled professionals in this field is soaring. Whether you're an experienced data engineer or just starting your journey, this book equips you with the knowledge and skills needed to excel in Azure data engineering. Inside this book, you will discover: ✔ Comprehensive Coverage: A deep dive into all the key concepts, tools, and best practices required for designing, building, and maintaining data solutions on Azure. ✔ Real-World Scenarios: Practical examples and case studies that illustrate how Azure is used to solve complex data challenges, making learning engaging and relevant. ✔ Exam-Ready Preparation: Thorough coverage of DP-203 exam objectives, complete with practice questions and expert tips to ensure you're well-prepared for exam day. ✔ Proven Expertise: Authored by Azure data engineering professionals who hold the certification and have hands-on experience in developing data solutions, offering you invaluable insights and practical guidance. Whether you aspire to advance your career, validate your expertise, or simply become a proficient Azure Data Engineer, Microsoft Certified Exam Guide - Azure Data Engineer Associate (DP-203) is your trusted companion on this journey. Don't miss this opportunity to become a sought-after data engineering expert in a competitive job market. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| dp 203 data engineering: Azure Data Engineering Cookbook Ahmad Osama, 2021-04-05 Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily Key FeaturesBuild highly efficient ETL pipelines using the Microsoft Azure Data servicesCreate and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data ExplorerDesign and execute batch processing solutions using Azure Data FactoryBook Description Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You'll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure. What you will learnUse Azure Blob storage for storing large amounts of unstructured dataPerform CRUD operations on the Cosmos Table APIImplement elastic pools and business continuity with Azure SQL DatabaseIngest and analyze data using Azure Synapse AnalyticsDevelop Data Factory data flows to extract data from multiple sourcesManage, maintain, and secure Azure Data Factory pipelinesProcess streaming data using Azure Stream Analytics and Data ExplorerWho this book is for This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed. |
| dp 203 data engineering: Beginning C# and .NET Benjamin Perkins, Jon D. Reid, 2021-07-09 Get a running start to learning C# programming with this fun and easy-to-read guide As one of the most versatile and powerful programming languages around, you might think C# would be an intimidating language to learn. It doesn’t have to be! In Beginning C# and .NET: 2021 Edition, expert Microsoft programmer and engineer Benjamin Perkins and program manager Jon D. Reid walk you through the precise, step-by-step directions you’ll need to follow to become fluent in the C# language and .NET. Using the proven WROX method, you’ll discover how to understand and write simple expressions and functions, debug programs, work with classes and class members, work with Windows forms, program for the web, and access data. You’ll even learn about some of the new features included in the latest releases of C# and .NET, including data consumption, code simplification, and performance. The book also offers: Detailed discussions of programming basics, like variables, flow control, and object-oriented programming that assume no previous programming experience “Try it Out” sections to help you write useful programming code using the steps you’ve learned in the book Downloadable code examples from wrox.com Perfect for beginning-level programmers who are completely new to C#, Beginning C# and .NET: 2021 Edition is a must-have resource for anyone interested in learning programming and looking for a fun and intuitive place to start. |
| dp 203 data engineering: Microsoft Azure Architect Technologies and Design Complete Study Guide Benjamin Perkins, William Panek, 2021-01-13 Become a proficient Microsoft Azure solutions architect Azure certifications are critical to the millions of IT professionals Microsoft has certified as MCSE and MCSA in Windows Server in the last 20 years. All of these professionals need to certify in key Azure exams to stay current and advance in their careers. Exams AZ-303 and AZ-304 are the key solutions architect exams that experienced Windows professionals will find most useful at the intermediate and advanced points of their careers. Microsoft Azure Architect Technologies and Design Complete Study Guide Exams AZ-303 and AZ-304 covers the two critical Microsoft Azure exams that intermediate and advanced Microsoft IT professionals will need to show proficiency as their organizations move to the Azure cloud. Understand Azure Set up your Microsoft Cloud network Solve real-world problems Get the confidence to pass the exam By learning all of these things plus using the Study Guide review questions and practice exams, the reader will be ready to take the exam and perform the job with confidence. |
| dp 203 data engineering: Azure Data Scientist Associate Certification Guide Andreas Botsikas, Michael Hlobil, 2021-12-03 Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily. |
| dp 203 data engineering: Exam Ref DP-900 Microsoft Azure Data Fundamentals Daniel A. Seara, Francesco Milano, 2021-03-12 Prepare for Microsoft Exam DP-900 Demonstrate your real-world foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services. Designed for business users, functional consultants, and other professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Fundamentals level. Focus on the expertise measured by these objectives: Describe core data concepts Describe how to work with relational data on Azure Describe how to work with non-relational data on Azure Describe an analytics workload on Azure This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have foundational knowledge of core data concepts and their implementation with Microsoft Azure data services, and are beginning to work with data in the cloud About the Exam Exam DP-900 focuses on core knowledge for describing fundamental database concepts and skills for cloud environments; cloud data services within Azure; cloud data roles, tasks, and responsibilities; Azure relational and non-relational data offerings, provisioning, and deployment; querying Azure relational databases; working with Azure non-relational data stores; building modern Azure data analytics solutions; and exploring Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Fundamentals certification, demonstrating your understanding of the core capabilities of Azure data services and their use with relational data, non-relational data, and analytics workloads. See full details at: www.microsoft.com/learn |
| dp 203 data engineering: Exam Ref AZ-900 Microsoft Azure Fundamentals Jim Cheshire, 2022-08-15 Prepare for the updated version of Microsoft Exam AZ-900 and help demonstrate your real-world knowledge of cloud services and how they can be provided with Microsoft Azure, including high-level concepts that apply throughout Azure, and key concepts specific to individual services. Designed for professionals in both non-technical or technical roles, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Fundamentals level. Focus on the expertise measured by these objectives: Describe cloud concepts Describe Azure architecture and services Describe Azure management and governance This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you want to show foundational knowledge of cloud services and their delivery with Microsoft Azure About the Exam Exam AZ-900 focuses on knowledge needed to describe cloud computing; the benefits of using cloud services; cloud service types; core Azure architectural components; Azure compute, networking, and storage services; Azure identity, access, and security; Azure cost management; Azure features and tools for governance and compliance, and for managing and deploying resources; and Azure monitoring tools. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Fundamentals credential, validating your basic knowledge of cloud services and how those services are provided with Azure. Whether you're new to the fi eld or a seasoned professional, demonstrating this knowledge can help you jump-start your career and prepare you to dive deeper into the many technical opportunities Azure offers. |
| dp 203 data engineering: Microsoft Certified Azure Fundamentals Study Guide James Boyce, 2021-04-13 Quickly preps technical and non-technical readers to pass the Microsoft AZ-900 certification exam Microsoft Certified Azure Fundamentals Study Guide: Exam AZ-900 is your complete resource for preparing for the AZ-900 exam. Microsoft Azure is a major component of Microsoft’s cloud computing model, enabling organizations to host their applications and related services in Microsoft’s data centers, eliminating the need for those organizations to purchase and manage their own computer hardware. In addition, serverless computing enables organizations to quickly and easily deploy data services without the need for servers, operating systems, and supporting systems. This book is targeted at anyone who is seeking AZ-900 certification or simply wants to understand the fundamentals of Microsoft Azure. Whatever your role in business or education, you will benefit from an understanding of Microsoft Azure fundamentals. Readers will also get one year of FREE access to Sybex’s superior online interactive learning environment and test bank, including hundreds of questions, a practice exam, electronic flashcards, and a glossary of key terms. This book will help you master the following topics covered in the AZ-900 certification exam: Cloud concepts Cloud types (Public, Private, Hybrid) Azure service types (IaaS, SaaS, PaaS) Core Azure services Security, compliance, privacy, and trust Azure pricing levels Legacy and modern lifecycles Growth in the cloud market continues to be very strong, and Microsoft is poised to see rapid and sustained growth in its cloud share. Written by a long-time Microsoft insider who helps customers move their workloads to and manage them in Azure on a daily basis, this book will help you break into the growing Azure space to take advantage of cloud technologies. |
| dp 203 data engineering: Getting Started with Bicep Freek Berson, 2021-07-07 This book is your guide to mastering Bicep! It contains practical solutions and examples to help you jump start your journey towards Infrastructure as Code for Azure! Book Description Infrastructure as Code is crucial to becoming successful in the Azure Cloud. Azure Resource Manager allows you to create resources in Azure in a declarative way. For many years we have been using ARM Templates to declare resources in a JSON format. Although ARM Templates are very powerful, the implementation of the JSON language is hard to read, maintain and debug. Bicep, a Domain Specific Language, overcomes these issues by providing a transparent abstraction layer on top of ARM and ARM Templates. This significantly improves the authoring experience. Bicep is easy to understand at a glance and straightforward to learn regardless of your experience with other programming languages. The book starts with some history and background in Infrastructure as Code and ARM Templates. It continues by explaining Bicep and providing guidance on how to get started. After the introduction, you will start your journey by understanding the syntax of Bicep. You will start by learning the basics first and you will gradually dive deeper in the more advanced scenarios. The book also contains a dedicated chapter on a big real-world example which provides you with great insights on how to leverage Bicep for production usage. Part of this book is also the Bicep playground, visualizer and a PowerShell module for Bicep provided by the community. Sample code used in this book is available on a dedicated GitHub repository. What you will learn How to get started with the Bicep CLI and VSCode Extension Deploying Bicep files to Azure, including template specs Understanding the Bicep file structure How to use the basic concepts of variables, parameters, tags, decorators, expressions, and symbolic names Getting familiar with more advanced topics like dependencies, loops, conditions, target scopes, modules, and nesting Leveraging features like snippets, scaffolding, and linter that support you while authoring Bicep templates. Who this book is intended for DevOps engineers, developers, consultants, and Azure architects with or without experience in ARM Templates and infrastructure as code looking to get started with Bicep. Table of Contents 1 Why this book 2 What is project bicep 3 Getting started 4 Bicep file structure explained 5 Deploying bicep files to azure 6 Bicep syntax 7 Bicep playground and example code 8 Bicep visualizer 9 Template specs 10 Guest Chapter: Bicep PowerShell module 11 A real-world example 12 Alternatives to Bicep 13 Closing Notes 14 About the author |
| dp 203 data engineering: Rosie Revere, Engineer Andrea Beaty, 2013-09-03 In this beloved New York Times bestselling picture book, meet Rosie Revere, a seemingly quiet girl by day but a brilliant inventor of gizmos and gadgets by night. Rosie dreams of becoming a great engineer, and her room becomes a secret workshop where she constructs ingenious inventions from odds and ends. From hot dog dispensers to helium pants and python-repelling cheese hats, Rosie's creations would astound anyone—if only she'd let them see. But Rosie is afraid of failure, so she hides her inventions under her bed. That is, until her great-great-aunt Rose (also known as Rosie the Riveter) pays her a visit. Aunt Rose teaches Rosie that the first flop isn't something to fear; it's something to celebrate. Failure only truly happens if you quit. And so, Rosie learns to embrace her passion, celebrate her missteps, and pursue her dreams with persistence. This empowering picture book encourages young readers to explore their creativity, persevere through challenges, and celebrate the journey toward achieving their goals. Whether you're a budding engineer or simply love stories of resilience, Rosie Revere, Engineer is a delightful read for all ages. Add this inspiring tale to your family library and discover the magic of celebrating each failure on the road to success. Don’t miss the book that the Duchess of York recently chose to read aloud at a Literally Healing visit to a children’s hospital. For more STEM-themed adventures, check out other titles by Andrea Beaty and David Roberts, including Ada Twist, Scientist, Iggy Peck, Architect, and Rosie Revere and the Raucous Riveters. “Will no doubt inspire conversations with children about the benefits of failure and the pursuit of dreams.” —School Library Journal Check out all the books in the Questioneers Series: The Questioneers Picture Book Series: Iggy Peck, Architect | Rosie Revere, Engineer | Ada Twist, Scientist | Sofia Valdez, Future Prez | Aaron Slater, Illustrator | Lila Greer, Teacher of the Year The Questioneers Chapter Book Series: Rosie Revere and the Raucous Riveters | Ada Twist and the Perilous Pants | Iggy Peck and the Mysterious Mansion | Sofia Valdez and the Vanishing Vote | Ada Twist and the Disappearing Dogs | Aaron Slater and the Sneaky Snake Questioneers: The Why Files Series: Exploring Flight! | All About Plants! | The Science of Baking | Bug Bonanza! | Rockin’ Robots! Questioneers: Ada Twist, Scientist Series: Ghost Busted | Show Me the Bunny | Ada Twist, Scientist: Brainstorm Book | 5-Minute Ada Twist, Scientist Stories The Questioneers Big Project Book Series: Iggy Peck’s Big Project Book for Amazing Architects | Rosie Revere’s Big Project Book for Bold Engineers | Ada Twist’s Big Project Book for Stellar Scientists | Sofia Valdez’s Big Project Book for Awesome Activists | Aaron Slater’s Big Project Book for Astonishing Artists |
| dp 203 data engineering: Azure Data Engineer Associate Certification Guide Giacinto Palmieri, Surendra Mettapalli, Newton Alex, 2024-05-23 Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF Key Features Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources Gain up-to-date skills to thrive in the dynamic world of cloud data engineering Build secure and sustainable data solutions using Azure services Book DescriptionOne of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.What you will learn Design and implement data lake solutions with batch and stream pipelines Secure data with masking, encryption, RBAC, and ACLs Perform standard extract, transform, and load (ETL) and analytics operations Implement different table geometries in Azure Synapse Analytics Write Spark code, design ADF pipelines, and handle batch and stream data Use Azure Databricks or Synapse Spark for data processing using Notebooks Leverage Synapse Analytics and Purview for comprehensive data exploration Confidently manage VMs, VNETS, App Services, and more Who this book is for This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book. A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered. |
| dp 203 data engineering: Feedback Systems Karl Johan Åström, Richard M. Murray, 2021-02-02 The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory |
| dp 203 data engineering: Mathematics of Information and Coding Te Sun Han, Kingo Kobayashi, 2002 This book is intended to provide engineering and/or statistics students, communications engineers, and mathematicians with the firm theoretic basis of source coding (or data compression) in information theory. Although information theory consists of two main areas, source coding and channel coding, the authors choose here to focus only on source coding. The reason is that, in a sense, it is more basic than channel coding, and also because of recent achievements in source coding and compression. An important feature of the book is that whenever possible, the authors describe universal coding methods, i.e., the methods that can be used without prior knowledge of the statistical properties of the data. The authors approach the subject of source coding from the very basics to the top frontiers in an intuitively transparent, but mathematically sound, manner. The book serves as a theoretical reference for communication professionals and statisticians specializing in information theory. It will also serve as an excellent introductory text for advanced-level and graduate students taking elementary or advanced courses in telecommunications, electrical engineering, statistics, mathematics, and computer science. |
| dp 203 data engineering: MCA Microsoft Certified Associate Azure Data Engineer Study Guide Benjamin Perkins, 2023-08-02 Prepare for the Azure Data Engineering certification—and an exciting new career in analytics—with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you’ll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you’ll get up to speed quickly and efficiently with Sybex’s easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the field Indispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxiety Complimentary access to Sybex’s expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech. |
| dp 203 data engineering: The Definitive Guide to Azure Data Engineering Ron C. L'Esteve, 2021-08-24 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 Learn Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory Create data ingestion pipelines that integrate control tables for self-service ELT Implement a reusable logging framework that can be applied to multiple pipelines Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools Transform data with Mapping Data Flows in Azure Data Factory Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics Get started with a variety of Azure data services through hands-on examples Who This Book Is For Data 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 |
| dp 203 data engineering: Implementing Azure Solutions Florian Klaffenbach, Jan-Henrik Damaschke, Oliver Michalski, 2017-05-19 A practical guide that enhances your skills in implementing Azure solutions for your organization About This Book Confidently configure, deploy, and manage cloud services and virtual machines Implement a highly-secured environment and respond to threats with increased visibility This comprehensive guide is packed with exciting practical scenarios that enable you to implement Azure solutions with ease Who This Book Is For This book is for IT architects, system and network admins, and DevOps engineers who are aware of Azure solutions and want to implement them for their organization. What You Will Learn Implement virtual networks, network gateways, Site-to-Site VPN, ExpressRoute, routing, and network devices Understand the working of different storage accounts in Azure Plan, deploy, and secure virtual machines Deploy and manage Azure Containers Get familiar with some common Azure usage scenarios In Detail Microsoft Azure has numerous effective solutions that shape the future of any business. However, the major challenge that architects and administrators face are implementing these solutions appropriately. Our book focuses on various implementation scenarios that will help overcome the challenge of implementing Azure's solutions in a very efficient manner and will also help you to prepare for Microsoft Architect exam. You will not only learn how to secure a newly deployed Azure Active Directory but also get to know how Azure Active Directory Synchronization could be implemented. To maintain an isolated and secure environment so that you can run your virtual machines and applications, you will implement Azure networking services. Also to manage, access, and secure your confidential data, you will implement storage solutions. Toward the end, you will explore tips and tricks to secure your environment. By the end, you will be able to implement Azure solutions such as networking, storage, and cloud effectively. Style and approach This step-by-step guide focuses on implementing various Azure solutions for your organization. The motive is to provide a comprehensive exposure and ensure they can implement these solutions with ease. |
| dp 203 data engineering: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| dp 203 data engineering: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. |
| dp 203 data engineering: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| dp 203 data engineering: Hands-On Azure Data Platform Sagar Lad, Abhishek Mishra, Ashirwad Satapathi, 2022-02-10 Plan, build, deploy, and monitor data solutions on Azure KEY FEATURES ● Work with PostgreSQL, MySQL, and CosmosDB databases on Microsoft Azure. ● Work with whole data architecture, leverage Azure Storage, Azure Synapse, and Azure Data Lake. ● Data integration strategies with Azure Data Factory and Data Bricks. DESCRIPTION 'Hands-On Azure Data Platform' helps readers get a fundamental understanding of the Database, Data Warehouse, and Data Lake and their management on the Azure Data Platform. The book describes how to work efficiently with Relational and Non-Relational Databases, Azure Synapse Analytics, and Azure Data Lake. The readers will use Azure Databricks and Azure Data Factory to experience data processing and transformation. The book delves deeply into topics like continuous integration, continuous delivery, and the use of Azure DevOps. The book focuses on the integration of Azure DevOps with CI/CD pipelines for data ops solutions. The book teaches readers how to migrate data from an on-premises system or another cloud service provider to Azure. After reading the book, readers will develop end-to-end data solutions using the Azure data platform. Additionally, data engineers and ETL developers can streamline their ETL operations using various efficient Azure services. WHAT YOU WILL LEARN ● In-depth knowledge of the principles of the data warehouse and the data lake. ● Acquaint yourself with Azure Storage Files, Blobs, and Queues. ● Create relational databases on the Azure platform using SQL, PostgreSQL, and MySQL. ● With Cosmos DB, you can create extremely scalable databases and data warehouses. ● Utilize Azure Databricks and Data Factory to develop data integration solutions. WHO THIS BOOK IS FOR This book is designed for big data engineers, data architects, and cloud engineers who want to understand how to use the Azure Data Platform to build enterprise-grade solutions. Learning about databases and the Azure Data Platform would be helpful but not necessary. TABLE OF CONTENTS 1. Getting Started with the Azure Data Platform 2. Working with Relational Databases on Azure 3. Working with Azure Synapse Analytics 4. Working with Azure Data Lake 5. Working with Azure Cosmos DB 6. Working with Azure Databricks 7. Working with Azure Data Factory 8. DevOps with the Azure Data Platform 9. Planning and Migrating On-Premises Azure Workloads to the Azure Data platform 10. Design and Implement Data Solutions on Azure |
| dp 203 data engineering: Engineering Dynamics Oliver M. O'Reilly, 2019-02-23 This Primer is intended to provide the theoretical background for the standard undergraduate, mechanical engineering course in dynamics. The book contains several worked examples and summaries and exercises at the end of each chapter to aid readers in their understanding of the material. Teachers who wish to have a source of more detailed theory for the course, as well as graduate students who need a refresher course on undergraduate dynamics when preparing for certain first year graduate school examinations, and students taking the course will find the work very helpful. |
| dp 203 data engineering: Hands-On Data Warehousing with Azure Data Factory Christian Coté, Michelle Kamrat Gutzait, Giuseppe Ciaburro, 2018-05-31 Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions Key Features Combine the power of Azure Data Factory v2 and SQL Server Integration Services Design and enhance performance and scalability of a modern ETL hybrid solution Interact with the loaded data in data warehouse and data lake using Power BI Book Description ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick’s Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them. What you will learn Understand the key components of an ETL solution using Azure Data Factory and Integration Services Design the architecture of a modern ETL hybrid solution Implement ETL solutions for both on-premises and Azure data Improve the performance and scalability of your ETL solution Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services Who this book is for This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS. |
| dp 203 data engineering: Aeronautical Engineer's Data Book Cliff Matthews, 2001-10-17 Aeronautical Engineer's Data Bookis an essential handy guide containing useful up to date information regularly needed by the student or practising engineer. Covering all aspects of aircraft, both fixed wing and rotary craft, this pocket book provides quick access to useful aeronautical engineering data and sources of information for further in-depth information. - Quick reference to essential data - Most up to date information available |
| dp 203 data engineering: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| dp 203 data engineering: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow |
| dp 203 data engineering: The Book of Alternative Data Alexander Denev, Saeed Amen, 2020-07-21 The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users. |
| dp 203 data engineering: Ultimate Azure Data Engineering Ashish Agarwal, 2024-07-22 TAGLINE Discover the world of data engineering in an on-premises setting versus the Azure cloud KEY FEATURES ● Explore Azure data engineering from foundational concepts to advanced techniques, spanning SQL databases, ETL processes, and cloud-native solutions. ● Learn to implement real-world data projects with Azure services, covering data integration, storage, and analytics, tailored for diverse business needs. ● Prepare effectively for Azure data engineering certifications with detailed exam-focused content and practical exercises to reinforce learning. DESCRIPTION Embark on a comprehensive journey into Azure data engineering with “Ultimate Azure Data Engineering”. Starting with foundational topics like SQL and relational database concepts, you'll progress to comparing data engineering practices in Azure versus on-premises environments. Next, you will dive deep into Azure cloud fundamentals, learning how to effectively manage heterogeneous data sources and implement robust Extract, Transform, Load (ETL) concepts using Azure Data Factory, mastering the orchestration of data workflows and pipeline automation. The book then moves to explore advanced database design strategies and discover best practices for optimizing data performance and ensuring stringent data security measures. You will learn to visualize data insights using Power BI and apply these skills to real-world scenarios. Whether you're aiming to excel in your current role or preparing for Azure data engineering certifications, this book equips you with practical knowledge and hands-on expertise to thrive in the dynamic field of Azure data engineering. WHAT WILL YOU LEARN ● Master the core principles and methodologies that drive data engineering such as data processing, storage, and management techniques. ● Gain a deep understanding of Structured Query Language (SQL) and relational database management systems (RDBMS) for Azure Data Engineering. ● Learn about Azure cloud services for data engineering, such as Azure SQL Database, Azure Data Factory, Azure Synapse Analytics, and Azure Blob Storage. ● Gain proficiency to orchestrate data workflows, schedule data pipelines, and monitor data integration processes across cloud and hybrid environments. ● Design optimized database structures and data models tailored for performance and scalability in Azure. ● Implement techniques to optimize data performance such as query optimization, caching strategies, and resource utilization monitoring. ● Learn how to visualize data insights effectively using tools like Power BI to create interactive dashboards and derive data-driven insights. ● Equip yourself with the knowledge and skills needed to pass Microsoft Azure data engineering certifications. WHO IS THIS BOOK FOR? This book is tailored for a diverse audience including aspiring and current Azure data engineers, data analysts, and data scientists, along with database and BI developers, administrators, and analysts. It is an invaluable resource for those aiming to obtain Azure data engineering certifications. TABLE OF CONTENTS 1. Introduction to Data Engineering 2. Understanding SQL and RDBMS Concepts 3. Data Engineering: Azure Versus On-Premises 4. Azure Cloud Concepts 5. Working with Heterogenous Data Sources 6. ETL Concepts 7. Database Design and Modeling 8. Performance Best Practices and Data Security 9. Data Visualization and Application in Real World 10. Data Engineering Certification Guide Index |
| dp 203 data engineering: Azure Storage, Streaming, and Batch Analytics Richard L. Nuckolls, 2020-11-03 The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Storage, Streaming, and Batch Analytics teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system. Summary The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Storage, Streaming, and Batch Analytics teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Microsoft Azure provides dozens of services that simplify storing and processing data. These services are secure, reliable, scalable, and cost efficient. About the book Azure Storage, Streaming, and Batch Analytics shows you how to build state-of-the-art data solutions with tools from the Microsoft Azure platform. Read along to construct a cloud-native data warehouse, adding features like real-time data processing. Based on the Lambda architecture for big data, the design uses scalable services such as Event Hubs, Stream Analytics, and SQL databases. Along the way, you’ll cover most of the topics needed to earn an Azure data engineering certification. What's inside Configuring Azure services for speed and cost Constructing data pipelines with Data Factory Choosing the right data storage methods About the reader For readers familiar with database management. Examples in C# and PowerShell. About the author Richard Nuckolls is a senior developer building big data analytics and reporting systems in Azure. Table of Contents 1 What is data engineering? 2 Building an analytics system in Azure 3 General storage with Azure Storage accounts 4 Azure Data Lake Storage 5 Message handling with Event Hubs 6 Real-time queries with Azure Stream Analytics 7 Batch queries with Azure Data Lake Analytics 8 U-SQL for complex analytics 9 Integrating with Azure Data Lake Analytics 10 Service integration with Azure Data Factory 11 Managed SQL with Azure SQL Database 12 Integrating Data Factory with SQL Database 13 Where to go next |
| dp 203 data engineering: Exam 70-413 Designing and Implementing a Server Infrastructure Microsoft Official Academic Course, 2014-10-27 This Microsoft Official Academic Course (MOAC) IT Professional curriculum prepares certification students for success every step of the way. This 70-413 Designing and Implementing a Server Infrastructure exam course is the first of a series of two exams Microsoft Certified Solutions Associates (MCSE) candidates are required to pass to gain the MCSE: Windows Server 2012 and Windows Server 2012 R2 certification. These MCSE exams test the skills and knowledge necessary to design, implement, and maintain a Windows Server 2012 infrastructure in an enterprise scaled, highly virtualized environment. Passing these exams confirms students’ ability to plan, configure, and implement the Windows Server 2012 services, such as server deployment, server virtualization, and network access and infrastructure. This complete ready-to-teach MOAC program is mapped to all of the exam objectives. |
| dp 203 data engineering: Chemical Engineering Design Gavin Towler, Ray Sinnott, 2012-01-25 Chemical Engineering Design, Second Edition, deals with the application of chemical engineering principles to the design of chemical processes and equipment. Revised throughout, this edition has been specifically developed for the U.S. market. It provides the latest US codes and standards, including API, ASME and ISA design codes and ANSI standards. It contains new discussions of conceptual plant design, flowsheet development, and revamp design; extended coverage of capital cost estimation, process costing, and economics; and new chapters on equipment selection, reactor design, and solids handling processes. A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data, and Excel spreadsheet calculations, plus over 150 Patent References for downloading from the companion website. Extensive instructor resources, including 1170 lecture slides and a fully worked solutions manual are available to adopting instructors. This text is designed for chemical and biochemical engineering students (senior undergraduate year, plus appropriate for capstone design courses where taken, plus graduates) and lecturers/tutors, and professionals in industry (chemical process, biochemical, pharmaceutical, petrochemical sectors). New to this edition: - Revised organization into Part I: Process Design, and Part II: Plant Design. The broad themes of Part I are flowsheet development, economic analysis, safety and environmental impact and optimization. Part II contains chapters on equipment design and selection that can be used as supplements to a lecture course or as essential references for students or practicing engineers working on design projects. - New discussion of conceptual plant design, flowsheet development and revamp design - Significantly increased coverage of capital cost estimation, process costing and economics - New chapters on equipment selection, reactor design and solids handling processes - New sections on fermentation, adsorption, membrane separations, ion exchange and chromatography - Increased coverage of batch processing, food, pharmaceutical and biological processes - All equipment chapters in Part II revised and updated with current information - Updated throughout for latest US codes and standards, including API, ASME and ISA design codes and ANSI standards - Additional worked examples and homework problems - The most complete and up to date coverage of equipment selection - 108 realistic commercial design projects from diverse industries - A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data and Excel spreadsheet calculations plus over 150 Patent References, for downloading from the companion website - Extensive instructor resources: 1170 lecture slides plus fully worked solutions manual available to adopting instructors |
| dp 203 data engineering: Computational Topology for Data Analysis Tamal Krishna Dey, Yusu Wang, 2022-03-10 Topological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions – like zigzag persistence and multiparameter persistence – and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks. |
| dp 203 data engineering: Exam Ref Da-100 Analyzing Data with Microsoft Power Bi Daniil Maslyuk, 2021-03 Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft DA-100 Analyzing Data with Microsoft Power BI certification exam. Exam Ref DA-100 Analyzing Data with Microsoft Power BI offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on specific areas of expertise modern IT professionals need to demonstrate real-world mastery of Power BI data analysis and visualization. Coverage includes: Preparing data: acquiring, profiling, cleaning, transforming, and loading data Modeling data: designing and developing data models, creating measures with DAX, and optimizing model performance Visualizing data: creating reports and dashboards, and enriching reports for usability Analyzing data: enhancing reports to expose insights, and performing advanced analysis Deploying and maintaining deliverables: managing datasets; creating and managing workspaces Microsoft Exam Ref publications stand apart from third-party study guides because they: Provide guidance from Microsoft, the creator of Microsoft certification exams Target IT professional-level exam candidates with content focused on their needs, not one-size-fits-all content Streamline study by organizing material according to the exam's objective domain (OD), covering one functional group and its objectives in each chapter Feature Thought Experiments to guide candidates through a set of what if? scenarios, and prepare them more effectively for Pro-level style exam questions Explore big picture thinking around the planning and design aspects of the IT pro's job role For more information on Exam DA-100 and the Microsoft Certified: Data Analyst Associate credential, visit https: //docs.microsoft.com/en-us/learn/certifications/data-analyst-associate. |
| dp 203 data engineering: Creating a Data-Driven Organization Carl Anderson, 2015-07-23 What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models--Publisher's description. |
| dp 203 data engineering: Advanced Calculus (Revised Edition) Lynn Harold Loomis, Shlomo Zvi Sternberg, 2014-02-26 An authorised reissue of the long out of print classic textbook, Advanced Calculus by the late Dr Lynn Loomis and Dr Shlomo Sternberg both of Harvard University has been a revered but hard to find textbook for the advanced calculus course for decades.This book is based on an honors course in advanced calculus that the authors gave in the 1960's. The foundational material, presented in the unstarred sections of Chapters 1 through 11, was normally covered, but different applications of this basic material were stressed from year to year, and the book therefore contains more material than was covered in any one year. It can accordingly be used (with omissions) as a text for a year's course in advanced calculus, or as a text for a three-semester introduction to analysis.The prerequisites are a good grounding in the calculus of one variable from a mathematically rigorous point of view, together with some acquaintance with linear algebra. The reader should be familiar with limit and continuity type arguments and have a certain amount of mathematical sophistication. As possible introductory texts, we mention Differential and Integral Calculus by R Courant, Calculus by T Apostol, Calculus by M Spivak, and Pure Mathematics by G Hardy. The reader should also have some experience with partial derivatives.In overall plan the book divides roughly into a first half which develops the calculus (principally the differential calculus) in the setting of normed vector spaces, and a second half which deals with the calculus of differentiable manifolds. |
| dp 203 data engineering: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| dp 203 data engineering: Exam Ref AZ-203 Developing Solutions for Microsoft Azure Santiago Fernandez Munoz, 2019 |
为什么hdmi2.1带宽优于dp1.4，还是会推荐dp？ - 知乎
dp要想打过hdmi，还得把家里大人叫来。 dp 2.1就比hdmi 2.1强很多，如果支持ubr20（20gb），那么4通道就可以提供80gbp带宽，比hdmi 2.1的48gb带宽大了不少。 今年 …
What is the difference between px, dip, dp, and sp?
Jan 8, 2010 · Use dp if you want to mention width and height to grow & shrink dynamically based on screen sizes. if we give dp/dip, android will automatically calculate the pixel size on the …
What does %~dp0 mean, and how does it work? - Stack Overflow
Feb 18, 2011 · The ~dp special syntax between the % and the 0 basically says to expand the variable %0 to show the drive letter and path, which gives you the current directory containing …
HDMI 2.2官宣，DP 接口规格比 HDMI 更落后了吗？ - 知乎
dp标准目前的优势是带宽更好兼容性更多，而且不需要授权费用。 但它有一个问题，在家电产品上不被广泛使用，原因是传统影像媒体巨头多年来精心编织的关系保护网，笼罩了视频制作和播 …
android - Converting pixels to dp - Stack Overflow
Jan 5, 2011 · DP is the resolution when you only factor the physical size of the screen. When you use DP it will scale your layout to other similar sized screens with different pixel densities. …
DP和HDMI哪个好，有什么区别？ - 知乎
dp出来没几年又遇上智能手机发展，传统pc销量受到影响，在使用量上确实差一截。 最后，两者该怎么选择？ 我只给一个建议：电视用HDMI、电脑、投影这些用DP，毕竟说到根上，这是电视 …
Women who have been DP'd by two men, what was your …
This is a place for conversation, article links, advice, personal stories, amateur photos, and dating (no r4r), pertaining to MFM threesomes. Popular topics include, but are not limited to: …
How do you guys get good at DP? : r/leetcode - Reddit
It focuses on teaching you how to develop a DP solution incrementally. You will realize learning recursion (which is hard) is the stepping stone to solving DP questions (remember recurrent …
HDMI、DVI、VGA、DP 四种接口有什么区别？ - 知乎
4、DP线. DP线也就是DisplayPort线的简称，支持同时传输视频和音频，传输的是数字信号。DP是完全免费的，没有版税，所以在很多设备上有大量采用。 （1）接口尺寸. 主要有标准接口和迷 …
显示器是接DP还是接HDMI？ - 知乎
dp打包传输采用的是一个非常聪明的方法：数据包里不仅可以放视频信号，还可以放入其他的控制信号。 dp可以在不改变接口，不增加线路的情况下，有更好的扩展性，实现更多的新功能。 …
为什么hdmi2.1带宽优于dp1.4，还是会推荐dp？ - 知乎
dp要想打过hdmi，还得把家里大人叫来。 dp 2.1就比hdmi 2.1强很多，如果支持ubr20（20gb），那么4通道就可以提供80gbp带宽，比hdmi 2.1的48gb带宽大了不少。 今年 …
What is the difference between px, dip, dp, and sp?
Jan 8, 2010 · Use dp if you want to mention width and height to grow & shrink dynamically based on screen sizes. if we give dp/dip, android will automatically calculate the pixel size on the …
What does %~dp0 mean, and how does it work? - Stack Overflow
Feb 18, 2011 · The ~dp special syntax between the % and the 0 basically says to expand the variable %0 to show the drive letter and path, which gives you the current directory containing …
HDMI 2.2官宣，DP 接口规格比 HDMI 更落后了吗？ - 知乎
dp标准目前的优势是带宽更好兼容性更多，而且不需要授权费用。 但它有一个问题，在家电产品上不被广泛使用，原因是传统影像媒体巨头多年来精心编织的关系保护网，笼罩了视频制作和播 …
android - Converting pixels to dp - Stack Overflow
Jan 5, 2011 · DP is the resolution when you only factor the physical size of the screen. When you use DP it will scale your layout to other similar sized screens with different pixel densities. …
DP和HDMI哪个好，有什么区别？ - 知乎
dp出来没几年又遇上智能手机发展，传统pc销量受到影响，在使用量上确实差一截。 最后，两者该怎么选择？ 我只给一个建议：电视用HDMI、电脑、投影这些用DP，毕竟说到根上，这是电 …
Women who have been DP'd by two men, what was your …
This is a place for conversation, article links, advice, personal stories, amateur photos, and dating (no r4r), pertaining to MFM threesomes. Popular topics include, but are not limited to: …
How do you guys get good at DP? : r/leetcode - Reddit
It focuses on teaching you how to develop a DP solution incrementally. You will realize learning recursion (which is hard) is the stepping stone to solving DP questions (remember recurrent …
HDMI、DVI、VGA、DP 四种接口有什么区别？ - 知乎
4、DP线. DP线也就是DisplayPort线的简称，支持同时传输视频和音频，传输的是数字信号。DP是完全免费的，没有版税，所以在很多设备上有大量采用。 （1）接口尺寸. 主要有标准接口和 …
显示器是接DP还是接HDMI？ - 知乎
dp打包传输采用的是一个非常聪明的方法：数据包里不仅可以放视频信号，还可以放入其他的控制信号。 dp可以在不改变接口，不增加线路的情况下，有更好的扩展性，实现更多的新功能。 …
