Hands On Azure Data Studio

This book provides a detailed walk-through of Microsoft's Azure Data Studio, which represents a paradigm shift from Microsoft, encompassing the company's recent commitment to open-source, multi-platform, and highly extensible software. This book demonstrates that Azure Data Studio (ADS) is a complete 'software platform', enabling database professionals to: - Dramatically improve, simplify, and accelerate daily tasks- Take advantage of Jupyter Notebooks, and share via GitHub- Easily Support both Microsoft and non-Microsoft platforms- Seamlessly work with data in the Cloud, and On-Premise- Collaborate more easily with internal as well as external clients- Add Extensions to customize and add functionality to ADSThis book also reveals that ADS goes well beyond just the SQL query language. In fact, ADS provides an amazing language fusion of SQL, PowerShell, Python and more for a deeply integrated development environment. Whether you are a SQL Developer, Data Engineer, DBA, or other Data Professional this book will give you head-start with this new and exciting developer platform from Microsoft.

Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions Key Features Combine the power of Azure Data Factory v2 and SQL Server Integration Services Design and enhance performance and scalability of a modern ETL hybrid solution Interact with the loaded data in data warehouse and data lake using Power BI Book Description ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick’s Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them. What you will learn Understand the key components of an ETL solution using Azure Data Factory and Integration Services Design the architecture of a modern ETL hybrid solution Implement ETL solutions for both on-premises and Azure data Improve the performance and scalability of your ETL solution Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services Who this book is for This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS.

Plan, build, deploy, and monitor data solutions on Azure KEY FEATURES ● Work with PostgreSQL, MySQL, and CosmosDB databases on Microsoft Azure. ● Work with whole data architecture, leverage Azure Storage, Azure Synapse, and Azure Data Lake. ● Data integration strategies with Azure Data Factory and Data Bricks. DESCRIPTION 'Hands-On Azure Data Platform' helps readers get a fundamental understanding of the Database, Data Warehouse, and Data Lake and their management on the Azure Data Platform. The book describes how to work efficiently with Relational and Non-Relational Databases, Azure Synapse Analytics, and Azure Data Lake. The readers will use Azure Databricks and Azure Data Factory to experience data processing and transformation. The book delves deeply into topics like continuous integration, continuous delivery, and the use of Azure DevOps. The book focuses on the integration of Azure DevOps with CI/CD pipelines for data ops solutions. The book teaches readers how to migrate data from an on-premises system or another cloud service provider to Azure. After reading the book, readers will develop end-to-end data solutions using the Azure data platform. Additionally, data engineers and ETL developers can streamline their ETL operations using various efficient Azure services. WHAT YOU WILL LEARN ● In-depth knowledge of the principles of the data warehouse and the data lake. ● Acquaint yourself with Azure Storage Files, Blobs, and Queues. ● Create relational databases on the Azure platform using SQL, PostgreSQL, and MySQL. ● With Cosmos DB, you can create extremely scalable databases and data warehouses. ● Utilize Azure Databricks and Data Factory to develop data integration solutions. WHO THIS BOOK IS FOR This book is designed for big data engineers, data architects, and cloud engineers who want to understand how to use the Azure Data Platform to build enterprise-grade solutions. Learning about databases and the Azure Data Platform would be helpful but not necessary. TABLE OF CONTENTS 1. Getting Started with the Azure Data Platform 2. Working with Relational Databases on Azure 3. Working with Azure Synapse Analytics 4. Working with Azure Data Lake 5. Working with Azure Cosmos DB 6. Working with Azure Databricks 7. Working with Azure Data Factory 8. DevOps with the Azure Data Platform 9. Planning and Migrating On-Premises Azure Workloads to the Azure Data platform 10. Design and Implement Data Solutions on Azure

Choose the right Azure data service and correct model design for successful implementation of your data model with the help of this hands-on guide Key FeaturesDesign a cost-effective, performant, and scalable database in AzureChoose and implement the most suitable design for a databaseDiscover how your database can scale with growing data volumes, concurrent users, and query complexityBook Description Data is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation. Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory. By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution. What you will learnModel relational database using normalization, dimensional, or Data Vault modelingProvision and implement Azure SQL DB and Azure Synapse SQL PoolsDiscover how to model a Data Lake and implement it using Azure StorageModel a NoSQL database and provision and implement an Azure Cosmos DBUse Azure Data Factory to implement ETL/ELT processesCreate a star schema model using dimensional modelingWho this book is for This book is for business intelligence developers and consultants who work on (modern) cloud data warehousing and design and implement databases. Beginner-level knowledge of cloud data management is expected.

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.

Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology. SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a “learn by example” approach for Intelligent Performance, security, mission-critical availability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters. The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications. What You Will LearnImplement Big Data Clusters with SQL Server, Spark, and HDFS Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sourcesCombine SQL and Spark to build a machine learning platform for AI applicationsBoost your performance with no application changes using Intelligent PerformanceIncrease security of your SQL Server through Secure Enclaves and Data ClassificationMaximize database uptime through online indexing and Accelerated Database RecoveryBuild new modern applications with Graph, ML Services, and T-SQL Extensibility with JavaImprove your ability to deploy SQL Server on Linux Gain in-depth knowledge to run SQL Server with containers and KubernetesKnow all the new database engine features for performance, usability, and diagnosticsUse the latest tools and methods to migrate your database to SQL Server 2019Apply your knowledge of SQL Server 2019 to Azure Who This Book Is For IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability.

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 the latest Azure ETL techniques both on-premises and in the cloud using Azure services such as SQL Server Integration Services (SSIS), Azure Data Factory, and Azure Databricks Key FeaturesUnderstand the key components of an ETL solution using Azure Integration ServicesDiscover the common and not-so-common challenges faced while creating modern and scalable ETL solutionsProgram and extend your packages to develop efficient data integration and data transformation solutionsBook Description ETL is one of the most common and tedious procedures for moving and processing data from one database to another. With the help of this book, you will be able to speed up the process by designing effective ETL solutions using the Azure services available for handling and transforming any data to suit your requirements. With this cookbook, you’ll become well versed in all the features of SQL Server Integration Services (SSIS) to perform data migration and ETL tasks that integrate with Azure. You’ll learn how to transform data in Azure and understand how legacy systems perform ETL on-premises using SSIS. Later chapters will get you up to speed with connecting and retrieving data from SQL Server 2019 Big Data Clusters, and even show you how to extend and customize the SSIS toolbox using custom-developed tasks and transforms. This ETL book also contains practical recipes for moving and transforming data with Azure services, such as Data Factory and Azure Databricks, and lets you explore various options for migrating SSIS packages to Azure. Toward the end, you’ll find out how to profile data in the cloud and automate service creation with Business Intelligence Markup Language (BIML). By the end of this book, you’ll have developed the skills you need to create and automate ETL solutions on-premises as well as in Azure. What you will learnExplore ETL and how it is different from ELTMove and transform various data sources with Azure ETL and ELT servicesUse SSIS 2019 with Azure HDInsight clustersDiscover how to query SQL Server 2019 Big Data Clusters hosted in AzureMigrate SSIS solutions to Azure and solve key challenges associated with itUnderstand why data profiling is crucial and how to implement it in Azure DatabricksGet to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutionsWho this book is for This book is for data warehouse architects, ETL developers, or anyone who wants to build scalable ETL applications in Azure. Those looking to extend their existing on-premise ETL applications to use big data and a variety of Azure services or others interested in migrating existing on-premise solutions to the Azure cloud platform will also find the book useful. Familiarity with SQL Server services is necessary to get the most out of this book.