Cloud Scale Analytics With Azure Data Services

A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key FeaturesStore and analyze data with enterprise-grade security and auditingPerform batch, streaming, and interactive analytics to optimize your big data solutions with easeDevelop and run parallel data processing programs using real-world enterprise scenariosBook Description Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learnImplement data governance with Azure servicesUse integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure MonitorExplore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wranglingImplement networking with Synapse Analytics and Spark poolsCreate and run Spark jobs with Databricks clustersImplement streaming using Azure Functions, a serverless runtime environment on AzureExplore the predefined ML services in Azure and use them in your appWho this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.

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.

Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows

Explore and work with various Microsoft Azure services for real-time Data Analytics KEY FEATURESÊ Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is builtÊ Go through some Data Analytics real-world use cases ÊÊ DESCRIPTIONÊ Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. WHAT WILL YOU LEARNÊÊ Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Ê WHO THIS BOOK IS FORÊ This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Ê TABLE OF CONTENTSÊÊ 1. Ê Data and its power 2. Ê Evolution of Analytics and its Types 3. Ê Internet of Things 4. Ê AI and ML 5. Ê Why cloud 6. Ê What are a data lake and a modern datamart 7. Ê Introduction to Azure services 8. Ê Types of data 9. Ê Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13.Ê Synapse Analytics 14.Ê Azure Databricks 15.Ê Azure Analysis Services 16.Ê Power BI 17.Ê Azure Machine Learning 18.Ê Sample Architectures and synergies - Real-Time and Batch 19.Ê Azure Data Catalog 20.Ê Azure Active Directory 21.Ê Azure Webapps 22.Ê Power apps 23.Ê Time Series Insights 24.Ê Azure Cognitive Services 25.Ê Azure Logicapps 26.Ê Azure VM 27.Ê Azure Functions 28.Ê Azure Containers 29.Ê Azure KubernetesÊ Service 30.Ê Use Case 1 31.Ê Use Case 2

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.

Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution. You’ll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs. Understand the fundamental patterns of the data lake and lambda architecture Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs Understand where Azure Machine Learning fits into your analytics pipeline Gain experience using these services on real-world data that has real-world problems, with scenarios ranging from aviation to Internet of Things (IoT)

Write efficient and powerful KQL queries to query and visualize your data and implement best practices to improve KQL execution performance Key FeaturesApply Azure Data Explorer best practices to manage your data at scale and reduce KQL execution timeDiscover how to query and visualize your data using the powerful KQLManage cluster performance and monthly costs by understanding how to size your ADX cluster correctlyBook Description Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters. The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance. By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI. What you will learnBecome well-versed with the core features of the Azure Data Explorer architectureDiscover how ADX can help manage your data at scale on AzureGet to grips with deploying your ADX environment and ingesting and analyzing your dataExplore KQL and learn how to query your dataQuery and visualize your data using the ADX UI and Power BIIngest structured and unstructured data types from an array of sourcesUnderstand how to deploy, scale, secure, and manage ADXWho this book is for This book is for data analysts, data engineers, and data scientists who are responsible for analyzing and querying their team's large volumes of data on Azure. SRE and DevOps engineers who deploy, maintain, and secure infrastructure will also find this book useful. Prior knowledge of Azure and basic data querying will help you to get the most out of this book.

Build scalable ETL data pipelines in the cloud using Azure Data Factory’s Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF’s code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and analytics systems. The book begins with an introduction to Azure Data Factory followed by an introduction to its Mapping Data Flows feature set. Subsequent chapters show how to build your first pipeline and corresponding data flow, implement common design patterns, and operationalize your result. By the end of the book, you will be able to apply what you’ve learned to your complex data integration and ETL projects in Azure. These projects will enable cloud-scale big analytics and data loading and transformation best practices for data warehouses. What You Will Learn Build scalable ETL jobs in Azure without writing code Transform big data for data quality and data modeling requirements Understand the different aspects of Azure Data Factory ETL pipelines from datasets and Linked Services to Mapping Data Flows Apply best practices for designing and managing complex ETL data pipelines in Azure Data Factory Add cloud-based ETL patterns to your set of data engineering skills Build repeatable code-free ETL design patterns Who This Book Is For Data engineers who are new to building complex data transformation pipelines in the cloud with Azure; and data engineers who need ETL solutions that scale to match swiftly growing volumes of data