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| azure synapse analytics architecture diagram: Mastering Azure Synapse Analytics , 2023-04-15 A practical guide that will help you transform your data into actionable insights with Azure Synapse Analytics KEY FEATURES ● Explore the different features in the Azure Synapse Analytics workspace. ● Learn how to integrate Power BI and Data Governance capabilities with Azure Synapse Analytics. ● Accelerate your analytics journey with the no-code/low-code capabilities of Azure Synapse. DESCRIPTION Cloud analytics is a crucial aspect of any digital transformation initiative, and the capabilities of the Azure Synapse analytics platform can simplify and streamline this process. By mastering Azure Synapse Analytics, analytics developers across organizations can boost their productivity by utilizing low-code, no-code, and traditional code-based analytics frameworks. This book starts with a comprehensive introduction to Azure Synapse Analytics and its limitless cloud-scale analytics capabilities. You will then learn how to explore and work with data warehousing features in Azure Synapse. Moving on, the book will guide you on how to effectively use Synapse Spark for data engineering and data science. It will help you learn how to gain insights from your data through Observational analytics using Synapse Data Explorer. You will also discover the seamless data integration capabilities of Synapse Pipeline, and delve into the benefits of Synapse Analytics' low-code and no-code pipeline development features. Lastly the book will show you how to create network topology and implement industry-specific architecture patterns in Azure Synapse Analytics. By the end of the book, you will be able to process and analyze vast amounts of data in real-time to gain insights quickly and make informed decisions. WHAT YOU WILL LEARN ● Leverage Synapse Spark for machine learning tasks. ● Use Synapse Data Explorer for telemetry analysis. ● Take advantage of Synapse's common data model-based database templates. ● Query data using T-SQL, KQL, and Spark SQL within Synapse. ● Integrate Microsoft Purview with Synapse for enhanced data governance. WHO THIS BOOK IS FOR This book is designed for Cloud data engineers with prior experience in Azure cloud computing, as well as Chief Data Officers (CDOs) and Data professionals, who want to use this unified platform for data ingestion, data warehousing, and big data analytics. TABLE OF CONTENTS 1. Cloud Analytics Concept 2. Introduction to Azure Synapse Analytics 3. Modern Data Warehouse with the Synapse SQL Pool 4. Query as a Service- Synapse Serverless SQL 5. Synapse Spark Pool Capability 6. Synapse Spark and Data Science 7. Learning Synapse Data Explorer 8. Synapse Data Integration 9. Synapse Link for HTAP 10. Azure Synapse -Unified Analytics Service 11. Synapse Workspace Ecosystem Integration 12. Azure Synapse Network Topology 13. Industry Cloud Analytics |
| azure synapse analytics architecture diagram: Azure Data and AI Architect Handbook Olivier Mertens, Breght Van Baelen, 2023-07-31 Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book DescriptionWith data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What you will learn Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is forThis book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird’s-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended. |
| azure synapse analytics architecture diagram: Limitless Analytics with Azure Synapse Prashant Kumar Mishra, Mukesh Kumar, 2021-06-18 Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your data Key FeaturesLearn to ingest, prepare, manage, and serve data for immediate business requirementsBring enterprise data warehousing and big data analytics together to gain insights from your dataDevelop end-to-end analytics solutions using Azure SynapseBook Description Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks. What you will learnExplore the necessary considerations for data ingestion and orchestration while building analytical pipelinesUnderstand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflowsQuery data using various coding languages on Azure SynapseFocus on Synapse SQL and Synapse SparkManage and monitor resource utilization and query activity in Azure SynapseConnect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse StudioCreate and manage IP firewall rules in Azure SynapseWho this book is for This book is for data architects, data scientists, data engineers, and business analysts who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively. |
| azure synapse analytics architecture diagram: Azure Modern Data Architecture Anouar BEN ZAHRA, Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices. |
| azure synapse analytics architecture diagram: Exam Ref DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI Daniil Maslyuk, Justin Frebault, 2023-07-09 Prepare for Microsoft Exam DP-500 and demonstrate your real-world ability to design, create, and deploy enterprise-scale data analytics solutions. Designed for business intelligence developers, architects, data analysts, and other professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Enterprise Data Analyst Associate level. Focus on the expertise measured by these objectives: Implement and manage a data analytics environment Query and transform data Implement and manage data models Explore and visualize data This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are a business intelligence developer, architect, data engineer, data architect, data analyst, or another professional with Power BI and Azure experience. About the Exam Exam DP-500 focuses on knowledge needed to govern and administer data analytics environments; integrate analytics platforms into existing IT infrastructure; manage the analytics development lifecycle; query data with Azure Synapse Analytics; ingest and transform data with Power BI; design and build tabular models; optimize enterprise-scale data models; explore data with Azure Synapse Analytics; and visualize data with Power BI. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Enterprise Data Analyst Associate certification, demonstrating your knowledge of designing, creating, and deploying enterprise-scale data analytics solutions. Responsibilities include performing advanced data analytics at scale, collecting enterprise-level requirements for data analytics solutions that include Azure and Microsoft Power BI, advising on data governance and configuration for Power BI administration, monitoring data usage, and optimizing solution performance. See full details at: microsoft.com/learn |
| azure synapse analytics architecture diagram: Jumpstart Snowflake Dmitry Anoshin, Dmitry Shirokov, Donna Strok, 2019-12-20 Explore the modern market of data analytics platforms and the benefits of using Snowflake computing, the data warehouse built for the cloud. With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use. Snowflake was built specifically for the cloud and it is a true game changer for the analytics market. This book will help onboard you to Snowflake, present best practices to deploy, and use the Snowflake data warehouse. In addition, it covers modern analytics architecture and use cases. It provides use cases of integration with leading analytics software such as Matillion ETL, Tableau, and Databricks. Finally, it covers migration scenarios for on-premise legacy data warehouses. What You Will Learn Know the key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Who This Book Is For Those working with data warehouse and business intelligence (BI) technologies, and existing and potential Snowflake users |
| azure synapse analytics architecture diagram: Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond Brett Hargreaves, 2021-07-23 Master the Microsoft Azure platform and prepare for the AZ-304 certification exam by learning the key concepts needed to identify key stakeholder requirements and translate these into robust solutions Key FeaturesBuild secure and scalable solutions on the Microsoft Azure platformLearn how to design solutions that are compliant with customer requirementsWork with real-world scenarios to become a successful Azure architect, and prepare for the AZ-304 examBook Description The AZ-304 exam tests an architect's ability to design scalable, reliable, and secure solutions in Azure based on customer requirements. Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond offers complete, up-to-date coverage of the AZ-304 exam content to help you prepare for it confidently, pass the exam first time, and get ready for real-world challenges. This book will help you to investigate the need for good architectural practices and discover how they address common concerns for cloud-based solutions. You will work through the CloudStack, from identity and access through to infrastructure (IaaS), data, applications, and serverless (PaaS). As you make progress, you will delve into operations including monitoring, resilience, scalability, and disaster recovery. Finally, you'll gain a clear understanding of how these operations fit into the real world with the help of full scenario-based examples throughout the book. By the end of this Azure book, you'll have covered everything you need to pass the AZ-304 certification exam and have a handy desktop reference guide. What you will learnUnderstand the role of architecture in the cloudEnsure security through identity, authorization, and governanceFind out how to use infrastructure components such as compute, containerization, networking, and storage accountsDesign scalable applications and databases using web apps, functions, messaging, SQL, and Cosmos DBMaintain operational health through monitoring, alerting, and backupsDiscover how to create repeatable and reliable automated deploymentsUnderstand customer requirements and respond to their changing needsWho this book is for This book is for Azure Solution Architects who advise stakeholders and help translate business requirements into secure, scalable, and reliable solutions. Junior architects looking to advance their skills in the Cloud will also benefit from this book. Experience with the Azure platform is expected, and a general understanding of development patterns will be advantageous. |
| azure synapse analytics architecture diagram: Cloud Analytics with Microsoft Azure Has Altaiar, Jack Lee, Michael Peña, 2021-01-28 Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features Key FeaturesUpdated with the latest features and new additions to Microsoft AzureMaster the fundamentals of cloud analytics using AzureLearn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insightsBook Description Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse. Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization. What you will learnExplore the concepts of modern data warehouses and data pipelinesDiscover unique design considerations while applying a cloud analytics solutionDesign an end-to-end analytics pipeline on the cloudDifferentiate between structured, semi-structured, and unstructured dataChoose a cloud-based service for your data analytics solutionsUse Azure services to ingest, store, and analyze data of any scaleWho this book is for This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure. Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book. |
| azure synapse analytics architecture diagram: The Modern Data Warehouse in Azure Matt How, 2020-06-15 Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane development tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse Who This Book Is For Data warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge |
| azure synapse analytics architecture diagram: Optimizing Databricks Workloads Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi, 2021-12-24 Accelerate computations and make the most of your data effectively and efficiently on Databricks Key FeaturesUnderstand Spark optimizations for big data workloads and maximizing performanceBuild efficient big data engineering pipelines with Databricks and Delta LakeEfficiently manage Spark clusters for big data processingBook Description Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently. What you will learnGet to grips with Spark fundamentals and the Databricks platformProcess big data using the Spark DataFrame API with Delta LakeAnalyze data using graph processing in DatabricksUse MLflow to manage machine learning life cycles in DatabricksFind out how to choose the right cluster configuration for your workloadsExplore file compaction and clustering methods to tune Delta tablesDiscover advanced optimization techniques to speed up Spark jobsWho this book is for This book is for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial. |
| azure synapse analytics architecture diagram: MICROSOFT AZURE NARAYAN CHANGDER, 2024-05-16 THE MICROSOFT AZURE MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE MICROSOFT AZURE MCQ TO EXPAND YOUR MICROSOFT AZURE KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY. |
| azure synapse analytics architecture diagram: Microsoft Azure Data Solutions - An Introduction Daniel A. Seara, Francesco Milano, Danilo Dominici, 2021-07-14 Discover and apply the Azure platform's most powerful data solutions Cloud technologies are advancing at an accelerating pace, supplanting traditional relational and data warehouse storage solutions with novel, high-value alternatives. Now, three pioneering Azure Data consultants offer an expert introduction to the relational, non-relational, and data warehouse solutions offered by the Azure platform. Drawing on their extensive experience helping organizations get more value from the Microsoft Data Platform, the authors guide you through decision-making, implementation, operations, security, and more. Throughout, step-by-step tutorials and hands-on exercises prepare you to succeed, even if you have no cloud data experience. Three leading experts in Microsoft Azure Data Solutions show how to: Master essential concepts of data storage and processing in cloud environments Handle the changing responsibilities of data engineers moving to the cloud Get started with Azure data storage accounts and other data facilities Walk through implementing relational and non-relational data stores in Azure Secure data using the least-permissions principle, Azure Active Directory, role-based access control, and other methods Develop efficient Azure batch processing and streaming solutions Monitor Azure SQL databases, blob storage, data lakes, Azure Synapse Analytics, and Cosmos DB Optimize Azure data solutions by solving problems with storage, management, and service interactions About This Book For data engineers, systems engineers, IT managers, developers, database administrators, cloud architects, and other IT professionals Requires little or no knowledge about Azure tools and services for data analysis |
| azure synapse analytics architecture diagram: Microsoft Azure Fundamentals Certification and Beyond Steve Miles, 2022-01-07 Gain in-depth knowledge of Azure fundamentals that will make it easy for you to achieve AZ-900 certification Key Features Get fundamental knowledge of cloud concepts and the Microsoft Azure platform Explore practical exercises to gain experience of working with the Microsoft Azure platform in the real world Prepare to achieve AZ-900 certification on the first go with the help of simplified examples covered in the book Book DescriptionThis is the digital and cloud era, and Microsoft Azure is one of the top cloud computing platforms. It’s now more important than ever to understand how the cloud functions and the different services that can be leveraged across the cloud. This book will give you a solid understanding of cloud concepts and Microsoft Azure, starting by taking you through cloud concepts in depth, then focusing on the core Azure architectural components, solutions, and management tools. Next, you will understand security concepts, defense-in-depth, and key security services such as Network Security Groups and Azure Firewall, as well as security operations tooling such as Azure Security Center and Azure Sentinel. As you progress, you will understand how identity, governance, privacy, and compliance are managed in Azure. Finally, you will get to grips with cost management, service-level agreements, and service life cycles. Throughout, the book features a number of hands-on exercises to support the concepts, services, and solutions discussed. This provides you with a glimpse of real-world scenarios, before finally concluding with practice questions for AZ-900 exam preparation. By the end of this Azure book, you will have a thorough understanding of cloud concepts and Azure fundamentals, enabling you to pass the AZ-900 certification exam easily.What you will learn Explore cloud computing with Azure cloud Gain an understanding of the core Azure architectural components Acquire knowledge of core services and management tools on Azure Get up and running with security concepts, security operations, and protection from threats Focus on identity, governance, privacy, and compliance features Understand Azure cost management, SLAs, and service life cycles Who this book is for This Azure fundamentals book is both for those with technical backgrounds and non-technical backgrounds who want to learn and explore the field of cloud computing, especially with Azure. This book will also help anyone who wants to develop a good foundation for achieving advanced Azure certifications. There is no prerequisite for this book except a willingness to learn and explore cloud concepts and Microsoft Azure. |
| azure synapse analytics architecture diagram: Azure Data Factory Cookbook Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton, 2024-02-28 Data Engineers guide to solve real-world problems encountered while building and transforming data pipelines using Azure's data integration tool Key Features Solve real-world data problems and create data-driven workflows with ease using Azure Data Factory Build an ADF pipeline that operates on pre-built ML model and Azure AI Get up and running with Fabric Data Explorer and extend ADF with Logic Apps and Azure functions Book DescriptionThis new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.What you will learn Build and Manage data pipelines with ease using the latest version of ADF Configure, load data, and operate data flows with Azure Synapse Get up and running with Fabric Data Factory Working with Azure Data Factory and Azure Purview Create big data pipelines using Databricks and Delta tables Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions Learn industry-grade best practices for using Azure Data Factory Who this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite. |
| azure synapse analytics architecture diagram: Learn Azure Synapse Data Explorer Pericles (Peri) Rocha, 2023-02-17 A hands-on guide to working on use cases helping you ingest, analyze, and serve insightful data from IoT as well as telemetry data sources using Azure Synapse Data Explorer Free PDF included with this book Key FeaturesAugment advanced analytics projects with your IoT and application dataExpand your existing Azure Synapse environments with unstructured dataBuild industry-level projects on integration, experimentation, and dashboarding with Azure SynapseBook Description Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data. This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you'll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you'll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users. By the end of this book, you'll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data. What you will learnIntegrate Data Explorer pools with all other Azure Synapse servicesCreate Data Explorer pools with Azure Synapse Studio and Azure PortalIngest, analyze, and serve data to users using Azure Synapse pipelinesIntegrate Power BI and visualize data with Synapse StudioConfigure Azure Machine Learning integration in Azure SynapseManage cost and troubleshoot Data Explorer pools in Synapse AnalyticsSecure Synapse workspaces and grant access to Data Explorer poolsWho this book is for If you are a data engineer, data analyst, or business analyst working with unstructured data and looking to learn how to maximize the value of such data, this book is for you. If you already have experience working with Azure Synapse and want to incorporate unstructured data into your data science project, you'll also find plenty of useful information in this book. To maximize your learning experience, familiarity with data and performing simple queries using SQL or KQL is recommended. Basic knowledge of Python will help you get more from the examples. |
| azure synapse analytics architecture diagram: Microsoft Cybersecurity Architect Exam Ref SC-100 Dwayne Natwick, Rod Trent, 2023-01-06 Advance your knowledge of architecting and evaluating cybersecurity services to tackle day-to-day challenges Key Features Gain a deep understanding of all topics covered in the SC-100 exam Benefit from practical examples that will help you put your new knowledge to work Design a zero-trust architecture and strategies for data, applications, access management, identity, and infrastructure Book Description Microsoft Cybersecurity Architect Exam Ref SC-100 is a comprehensive guide that will help cybersecurity professionals design and evaluate the cybersecurity architecture of Microsoft cloud services. Complete with hands-on tutorials, projects, and self-assessment questions, you'll have everything you need to pass the SC-100 exam. This book will take you through designing a strategy for a cybersecurity architecture and evaluating the governance, risk, and compliance (GRC) of the architecture. This will include cloud-only and hybrid infrastructures, where you'll learn how to protect using the principles of zero trust, along with evaluating security operations and the overall security posture. To make sure that you are able to take the SC-100 exam with confidence, the last chapter of this book will let you test your knowledge with a mock exam and practice questions. By the end of this book, you'll have the knowledge you need to plan, design, and evaluate cybersecurity for Microsoft cloud and hybrid infrastructures, and pass the SC-100 exam with flying colors. What you will learn Design a zero-trust strategy and architecture Evaluate GRC technical strategies and security operations strategies Design security for infrastructure Develop a strategy for data and applications Understand everything you need to pass the SC-100 exam with ease Use mock exams and sample questions to prepare for the structure of the exam Who this book is for This book is for a wide variety of cybersecurity professionals – from security engineers and cybersecurity architects to Microsoft 365 administrators, user and identity administrators, infrastructure administrators, cloud security engineers, and other IT professionals preparing to take the SC-100 exam. It's also a good resource for those designing cybersecurity architecture without preparing for the exam. To get started, you'll need a solid understanding of the fundamental services within Microsoft 365, and Azure, along with knowledge of security, compliance, and identity capabilities in Microsoft and hybrid architectures. |
| azure synapse analytics architecture diagram: Microsoft Azure Essentials Azure Machine Learning Jeff Barnes, 2015-04-25 Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series. |
| azure synapse analytics architecture diagram: Azure Data Factory Cookbook Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton, 2020-12-24 Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory’s visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learnCreate an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Azure Data Lake and ADFBuild a machine learning app with Apache Spark and ADFMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected. |
| azure synapse analytics architecture diagram: Microsoft Azure Security Center Yuri Diogenes, Tom Shinder, 2018-06-04 Discover high-value Azure security insights, tips, and operational optimizations This book presents comprehensive Azure Security Center techniques for safeguarding cloud and hybrid environments. Leading Microsoft security and cloud experts Yuri Diogenes and Dr. Thomas Shinder show how to apply Azure Security Center’s full spectrum of features and capabilities to address protection, detection, and response in key operational scenarios. You’ll learn how to secure any Azure workload, and optimize virtually all facets of modern security, from policies and identity to incident response and risk management. Whatever your role in Azure security, you’ll learn how to save hours, days, or even weeks by solving problems in most efficient, reliable ways possible. Two of Microsoft’s leading cloud security experts show how to: • Assess the impact of cloud and hybrid environments on security, compliance, operations, data protection, and risk management • Master a new security paradigm for a world without traditional perimeters • Gain visibility and control to secure compute, network, storage, and application workloads • Incorporate Azure Security Center into your security operations center • Integrate Azure Security Center with Azure AD Identity Protection Center and third-party solutions • Adapt Azure Security Center’s built-in policies and definitions for your organization • Perform security assessments and implement Azure Security Center recommendations • Use incident response features to detect, investigate, and address threats • Create high-fidelity fusion alerts to focus attention on your most urgent security issues • Implement application whitelisting and just-in-time VM access • Monitor user behavior and access, and investigate compromised or misused credentials • Customize and perform operating system security baseline assessments • Leverage integrated threat intelligence to identify known bad actors |
| azure synapse analytics architecture diagram: Engineering Data Mesh in Azure Cloud Aniruddha Deswandikar, 2024-03-29 Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads Key Features Delve into core data mesh concepts and apply them to real-world situations Safely reassess and redesign your framework for seamless data mesh integration Conquer practical challenges, from domain organization to building data contracts Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDecentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.What you will learn Build a strategy to implement a data mesh in Azure Cloud Plan your data mesh journey to build a collaborative analytics platform Address challenges in designing, building, and managing data contracts Get to grips with monitoring and governing a data mesh Understand how to build a self-service portal for analytics Design and implement a secure data mesh architecture Resolve practical challenges related to data mesh adoption Who this book is for This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book. |
| azure synapse analytics architecture diagram: Exam Ref AZ-304 Microsoft Azure Architect Design Ashish Agrawal, Avinash Bhavsar, MJ Parker, Gurvinder Singh, 2021-07-21 Prepare for Microsoft Exam AZ-304—and help demonstrate your real-world mastery of designing and implementing solutions that run on Microsoft Azure, including key aspects such as compute, network, storage, and security. Designed for modern IT professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Expert level. Focus on the expertise measured by these objectives: • Design monitoring • Design identity and security • Design data storage • Design business continuity • Design infrastructure This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you are an IT professional with significant experience and knowledge of IT operations, and expert-level Azure administration skills, and experience with Azure development and DevOps processes About the Exam Exam AZ-304 focuses on knowledge needed to design for cost optimization; design logging and monitoring solutions; design authentication, authorization, governance, and application security; design database solutions and data integrations; select storage accounts; design for backup/recovery and high availability; design compute and network infrastructure; design application architectures, and design migrations. About Microsoft Certification Passing this exam and Exam AZ-303: Microsoft Azure Architect Technologies fulfills your requirements for the Microsoft Certified: Azure Solutions Architect Expert credential, demonstrating your expertise in compute, network, storage, and security for designing and implementing modern cloud-based solutions that run on Microsoft Azure. See full details at: microsoft.com/learn |
| azure synapse analytics architecture diagram: Data Lake Analytics on Microsoft Azure Harsh Chawla, Pankaj Khattar, 2020-11-15 Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure. What Will You Learn You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight Who This Book Is For Data platform professionals, database architects, engineers, and solution architects |
| azure synapse analytics architecture diagram: 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. |
| azure synapse analytics architecture diagram: Beginning Azure Synapse Analytics Bhadresh Shiyal, 2021-09-26 Get started with Azure Synapse Analytics, Microsoft's modern data analytics platform. This book covers core components such as Synapse SQL, Synapse Spark, Synapse Pipelines, and many more, along with their architecture and implementation. The book begins with an introduction to core data and analytics concepts followed by an understanding of traditional/legacy data warehouse, modern data warehouse, and the most modern data lakehouse. You will go through the introduction and background of Azure Synapse Analytics along with its main features and key service capabilities. Core architecture is discussed, along with Synapse SQL. You will learn its main features and how to create a dedicated Synapse SQL pool and analyze your big data using Serverless Synapse SQL Pool. You also will learn Synapse Spark and Synapse Pipelines, with examples. And you will learn Synapse Workspace and Synapse Studio followed by Synapse Link and its features. You will go through use cases in Azure Synapse and understand the reference architecture for Synapse Analytics. After reading this book, you will be able to work with Azure Synapse Analytics and understand its architecture, main components, features, and capabilities. What You Will Learn Understand core data and analytics concepts and data lakehouse concepts Be familiar with overall Azure Synapse architecture and its main components Be familiar with Synapse SQL and Synapse Spark architecture components Work with integrated Apache Spark (aka Synapse Spark) and Synapse SQL engines Understand Synapse Workspace, Synapse Studio, and Synapse Pipeline Study reference architecture and use cases Who This Book Is For Azure data analysts, data engineers, data scientists, and solutions architects |
| azure synapse analytics architecture diagram: Practical Guide to Azure Cognitive Services Chris Seferlis, Christopher Nellis, Andy Roberts, 2023-05-12 Streamline your complex processes and optimize your organization's operational efficiency, cost-effectiveness, and customer experience by unlocking the potential of Microsoft Azure Cognitive Services and OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Minimize costs and maximize operations by automating mundane activities using AI tools Ideate solutions using real-world examples for manufacturing process improvement with AI Master TCO and ROI analysis for implementing AI solutions, automating operations, and ideating innovative manufacturing solutions with real-world examples Book Description Azure Cognitive Services and OpenAI are a set of pre-built artificial intelligence (AI) solution APIs that can be leveraged from existing applications, allowing customers to take advantage of Microsoft's award-winning Vision, Speech, Text, Decision, and GPT-4 AI capabilities. With Practical Guide to Azure Cognitive Services, you'll work through industry-specific examples of implementations to get a head-start in your production journey. You'll begin with an overview of the categorization of Azure Cognitive Services and the benefits of embracing AI solutions for practical business applications. After that, you'll explore the benefits of using Azure Cognitive Services to optimize efficiency and improve predictive capabilities. Then, you'll learn how to leverage Vision capabilities for quality control, Form Recognizer to streamline supply chain nuances, language understanding to improve customer service, and Cognitive Search for next-generation knowledge-mining solutions. By the end of this book, you'll be able to implement various Cognitive Services solutions that will help you enhance efficiency, reduce costs, and improve the customer experience at your organization. You'll also be well equipped to automate mundane tasks by reaping the full potential of OpenAI. What you will learn Master cost-effective deployment of Azure Cognitive Services Develop proven solutions from an architecture and development standpoint Understand how Cognitive Services are deployed and customized Evaluate various uses of Cognitive Services with different mediums Disseminate Azure costs for Cognitive Services workloads smoothly Deploy next-generation Knowledge Mining solutions with Cognitive Search Explore the current and future journey of OpenAI Understand the value proposition of different AI projects Who this book is for This book is for data scientists, technology leaders, and software engineers looking to implement Azure Cognitive Services with the help of sample use cases derived from success stories. Experience with Python as well as an overall understanding of the Azure Portal with related services such as Azure Data Lake Storage and Azure Functions will help you make the most of this book. |
| azure synapse analytics architecture diagram: Agile Estimating and Planning Mike Cohn, 2005-11-01 Agile Estimating and Planning is the definitive, practical guide to estimating and planning agile projects. In this book, Agile Alliance cofounder Mike Cohn discusses the philosophy of agile estimating and planning and shows you exactly how to get the job done, with real-world examples and case studies. Concepts are clearly illustrated and readers are guided, step by step, toward how to answer the following questions: What will we build? How big will it be? When must it be done? How much can I really complete by then? You will first learn what makes a good plan-and then what makes it agile. Using the techniques in Agile Estimating and Planning, you can stay agile from start to finish, saving time, conserving resources, and accomplishing more. Highlights include: Why conventional prescriptive planning fails and why agile planning works How to estimate feature size using story points and ideal days–and when to use each How and when to re-estimate How to prioritize features using both financial and nonfinancial approaches How to split large features into smaller, more manageable ones How to plan iterations and predict your team's initial rate of progress How to schedule projects that have unusually high uncertainty or schedule-related risk How to estimate projects that will be worked on by multiple teams Agile Estimating and Planning supports any agile, semiagile, or iterative process, including Scrum, XP, Feature-Driven Development, Crystal, Adaptive Software Development, DSDM, Unified Process, and many more. It will be an indispensable resource for every development manager, team leader, and team member. |
| azure synapse analytics architecture diagram: Azure Databricks Cookbook Phani Raj, Vinod Jaiswal, 2021-09-17 Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key FeaturesIntegrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelinesUse Databricks SQL to run ad hoc queries on your data lake and create dashboardsProductionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environmentsBook Description Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What you will learnRead and write data from and to various Azure resources and file formatsBuild a modern data warehouse with Delta Tables and Azure Synapse AnalyticsExplore jobs, stages, and tasks and see how Spark lazy evaluation worksHandle concurrent transactions and learn performance optimization in Delta tablesLearn Databricks SQL and create real-time dashboards in Databricks SQLIntegrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelinesDiscover how to use RBAC and ACLs to restrict data accessBuild end-to-end data processing pipeline for near real-time data analyticsWho this book is for This recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book. |
| azure synapse analytics architecture diagram: 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 |
| azure synapse analytics architecture diagram: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| azure synapse analytics architecture diagram: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| azure synapse analytics architecture diagram: Full Stack Serverless Nader Dabit, 2020-07-13 Cloud computing is typically associated with backend development and DevOps. But with the rise of serverless technologies and a new generation of services and frameworks, frontend and mobile developers can build robust applications with production-ready features such as authentication and authorization, API gateways, chatbots, augmented reality scenes, and more. This hands-on guide shows you how. Nader Dabit, developer advocate at Amazon Web Services, guides you through the process of building full stack applications using React, AWS, GraphQL, and AWS Amplify. You’ll learn how to create and incorporate services into your client applications while learning general best practices, deployment strategies, rich media management, and continuous integration and delivery along the way. Learn how to build serverless applications that solve real problems Understand what is (and isn’t) possible when using these technologies Create a GraphQL API that interacts with DynamoDB and a NoSQL database Examine how authentication works—and learn the difference between authentication and authorization Get an in-depth view of how serverless functions work and why they’re important Build full stack applications on AWS and create offline apps with Amplify DataStore |
| azure synapse analytics architecture diagram: Mastering Cloud Computing Rajkumar Buyya, Christian Vecchiola, S.Thamarai Selvi, 2013-04-05 Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won't live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout. - Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment - Real-world case studies include scientific, business, and energy-efficiency considerations |
| azure synapse analytics architecture diagram: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| azure synapse analytics architecture diagram: SAP HANA 2.0 Denys Van Kempen, 2019 Enter the fast-paced world of SAP HANA 2.0 with this introductory guide. Begin with an exploration of the technological backbone of SAP HANA as a database and platform. Then, step into key SAP HANA user roles and discover core capabilities for administration, application development, advanced analytics, security, data integration, and more. No matter how SAP HANA 2.0 fits into your business, this book is your starting point. In this book, you'll learn about: a. Technology Discover what makes an in-memory database platform. Learn about SAP HANA's journey from version 1.0 to 2.0, take a tour of your technology options, and walk through deployment scenarios and implementation requirements. b. Tools Unpack your SAP HANA toolkit. See essential tools in action, from SAP HANA cockpit and SAP HANA studio, to the SAP HANA Predictive Analytics Library and SAP HANA smart data integration. c. Key Roles Understand how to use SAP HANA as a developer, administrator, data scientist, data center architect, and more. Explore key tasks like backend programming with SQLScript, security setup with roles and authorizations, data integration with the SAP HANA Data Management Suite, and more. Highlights include: 1) Architecture 2) Administration 3) Application development 4) Analytics 5) Security 6) Data integration 7) Data architecture 8) Data center |
| azure synapse analytics architecture diagram: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| azure synapse analytics architecture diagram: Microsoft Azure Essentials Migrating SQL Server Databases to Azure Carl Rabeler, 2016-06-06 Part of the “Microsoft Azure Essentials” series, this ebook helps SQL Server database users understand Microsoft’s offering for SQL Server in Azure. Learn how SQL Server in Azure is similar to SQL Server in an on-premises environment, and how they are different. The author, a content lead for Azure.com, walks you through the steps of getting started with SQL Server in an Azure virtual machine and with Azure SQL Database. Follow the numerous screenshots to create a trial subscription, create SQL Server in an Azure virtual machine, create an Azure SQL Database, migrate an on-premises database to each Azure environment, create users, back up and restore data, and archive data. |
| azure synapse analytics architecture diagram: 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. |
| azure synapse analytics architecture diagram: 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. |
| azure synapse analytics architecture diagram: 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 |
| azure synapse analytics architecture diagram: Big Data Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi, 2021-03-15 Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book. |
Executive Summary for Azure Synapse Analytics Cookbook
Azure Synapse Analytics is the right choice of strategic modern data service for your organization to maximize the value of existing SQL developer skillsets. This executive summary is designed …
Azure Synapse Analytics Architecture Diagram (PDF)
Power BI and Data Governance capabilities with Azure Synapse Analytics Accelerate your analytics journey with the no code low code capabilities of Azure Synapse DESCRIPTION …
Marketing at Scale in Microsoft - AI at Wharton
Diagram of Azure Synapse Analytics architecture. Azure Synapse ... Synapse SQL is a distributed query system for T-SQL that enables data warehousing. Synapse SQL offers both serverless …
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With the announcement of Azure Synapse Analytics that remove the barriers between data warehousing and big data analytics, we deprecated DES and built Azure Synapse Link for …
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Azure Synapse Analytics added multiple new features during Microsoft Ignite 2020. These new features bring accelerated time to insight with new built-in capabilities for both data exploration …
Azure Data Engineer Associate Certification Architecture …
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Azure Synapse Analytics Architecture Diagram - bubetech.com
Synapse Pipeline and delve into the benefits of Synapse Analytics low code and no code pipeline development features Lastly the book will show you how to create network topology …
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Introduces a new data management architecture that streamlines enterprise data systems and boosts innovation amid machine learning's transformative potential. Adds a transactional …
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Architecture patterns and virtual data warehousing. Explore patterns and get started with virtual data warehousing using Synapse Link. 3. Explore Cloud Data Warehousing and ingesting data …
Azure Synapse DW - Pool Best Practices & Field Guidance
Azure Synapse is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless …
Microsoft Azure Virtual Training Day: Delivering the modern …
for analytics Azure Storage/ Data Lake Store Azure Synapse Analytics Azure Databricks Azure Data Factory SQL DB Azure Data Factory Logs, files, and media (unstructured) Business and …
INTEGRATING MICROSTRATEGY 2021 WITH AZURE SYNAPSE …
This paper provides a brief overview of the MicroStrategy architecture and explains how this architecture takes advantage of the technology advances and business intelligence …
Toolkit: Get started uncovering critical insights with Azure …
Azure Synapse Analytics enables you to analyze data any way you want—whether you prefer to use Notebooks or SQL scripts, or if your data source is in a data lake or a database—all within …
Become Data Driven with Azure Synapse Analytics
You can start with below learning paths (free and digital): - Work with Data Warehouses using Azure Synapse Analytics https://docs.microsoft.com/en-us/learn/paths/work-with-data …
Efficiently Ingest All Your Data With Scale and Automation
Ingest and replicate data from relational databases such as Oracle, Microsoft SQL Server, and MySQL and move it onto Amazon S3, Kafka, Microsoft Azure Data Lake Storage, Microsoft …
Building Industry-Aware Analytics Solutions Using Azure …
Through this guide, you will gain a high-level understanding of Azure Synapse, especially as it relates to building industry-aware analytics solutions relevant to your industry. While the retail …
The Power BI Professional s Guide to Azure Synapse Analytics
Azure Synapse is an end-to-end cloud-native analytics platform that brings together data ingestion, data warehousing and big data into a single service. It gives you the freedom to …
Lakehouse Analytics with Microsoft Fabric and Azure …
The lakehouse architecture on Azure fully embraces the power of Apache Spark, a scalable analytics engine for enterprises. With AI assistants in both Fabric and Azure Databricks, teams …
Creating a Logical Data Warehouse using Azure Synapse …
Analyse CSV, Parquet & JSON data stored in Azure Storage using common T-SQL commands. Query Cosmos DB in real-time. Extract schema information to understand the structure of …
Implementing Enterprise-Wide Lakehouse using Microsoft …
Azure Databricks, powered by Apache Spark and Delta Lake, is an open-source storage layer that offers a modern platform for realizing enterprise-wide lakehouses. This paper outlines how …
Executive Summary for Azure Synapse Analytics Cookbook
Azure Synapse Analytics is the right choice of strategic modern data service for your organization to maximize the value of existing SQL developer skillsets. This executive summary is designed …
Azure Synapse Analytics Architecture Diagram (PDF)
Power BI and Data Governance capabilities with Azure Synapse Analytics Accelerate your analytics journey with the no code low code capabilities of Azure Synapse DESCRIPTION …
Marketing at Scale in Microsoft - AI at Wharton
Diagram of Azure Synapse Analytics architecture. Azure Synapse ... Synapse SQL is a distributed query system for T-SQL that enables data warehousing. Synapse SQL offers both serverless …
Ensure optimal transition from Data Export Service to Azure …
With the announcement of Azure Synapse Analytics that remove the barriers between data warehousing and big data analytics, we deprecated DES and built Azure Synapse Link for …
Azure Synapse Analytics - Big Data Conference Europe
Azure Synapse Analytics added multiple new features during Microsoft Ignite 2020. These new features bring accelerated time to insight with new built-in capabilities for both data exploration …
Azure Data Engineer Associate Certification Architecture …
Azure Data Engineer Associate Certification Course © B r a i n 4 c e E d u c a t i o n S o l u t i o n s P v t . L t d Page 10 Ingestion, ETL, and stream processing pipelines with Azure Databricks …
Azure Synapse Analytics Architecture Diagram
Synapse Pipeline and delve into the benefits of Synapse Analytics low code and no code pipeline development features Lastly the book will show you how to create network topology …
Cloud Scale Analytics with Azure Databricks and Microsoft …
Introduces a new data management architecture that streamlines enterprise data systems and boosts innovation amid machine learning's transformative potential. Adds a transactional …
Dynamics data in Synapse Link for Dataverse - Dynamics 365 …
Architecture patterns and virtual data warehousing. Explore patterns and get started with virtual data warehousing using Synapse Link. 3. Explore Cloud Data Warehousing and ingesting data …
Azure Synapse DW - Pool Best Practices & Field Guidance
Azure Synapse is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless …
Microsoft Azure Virtual Training Day: Delivering the modern …
for analytics Azure Storage/ Data Lake Store Azure Synapse Analytics Azure Databricks Azure Data Factory SQL DB Azure Data Factory Logs, files, and media (unstructured) Business and …
INTEGRATING MICROSTRATEGY 2021 WITH AZURE …
This paper provides a brief overview of the MicroStrategy architecture and explains how this architecture takes advantage of the technology advances and business intelligence …
Toolkit: Get started uncovering critical insights with Azure …
Azure Synapse Analytics enables you to analyze data any way you want—whether you prefer to use Notebooks or SQL scripts, or if your data source is in a data lake or a database—all within …
Become Data Driven with Azure Synapse Analytics
You can start with below learning paths (free and digital): - Work with Data Warehouses using Azure Synapse Analytics https://docs.microsoft.com/en-us/learn/paths/work-with-data …
Efficiently Ingest All Your Data With Scale and Automation
Ingest and replicate data from relational databases such as Oracle, Microsoft SQL Server, and MySQL and move it onto Amazon S3, Kafka, Microsoft Azure Data Lake Storage, Microsoft …
Building Industry-Aware Analytics Solutions Using Azure …
Through this guide, you will gain a high-level understanding of Azure Synapse, especially as it relates to building industry-aware analytics solutions relevant to your industry. While the retail …
The Power BI Professional s Guide to Azure Synapse Analytics
Azure Synapse is an end-to-end cloud-native analytics platform that brings together data ingestion, data warehousing and big data into a single service. It gives you the freedom to …
Lakehouse Analytics with Microsoft Fabric and Azure …
The lakehouse architecture on Azure fully embraces the power of Apache Spark, a scalable analytics engine for enterprises. With AI assistants in both Fabric and Azure Databricks, teams …
Creating a Logical Data Warehouse using Azure Synapse …
Analyse CSV, Parquet & JSON data stored in Azure Storage using common T-SQL commands. Query Cosmos DB in real-time. Extract schema information to understand the structure of …
Implementing Enterprise-Wide Lakehouse using Microsoft …
Azure Databricks, powered by Apache Spark and Delta Lake, is an open-source storage layer that offers a modern platform for realizing enterprise-wide lakehouses. This paper outlines how …
