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Discover how you can migrate a traditional on-premise SQL server database to a cloud-based solution with Microsoft Azure. Built with database administrators in mind, this book emulates different scenarios you might come across while working with large, complex SQL database migrations and provides solutions for effectively managing the migrated databases. Key Features Implement backup, restore, and recovery of Azure SQL databases Create shards and elastic pools to scale Azure SQL databases Automate common management tasks with PowerShell Implement over 40 practical activities and exercises across 24 topics to reinforce your learning Book Description As the cloud version of SQL Server, Azure SQL Database differs in key ways when it comes to management, maintenance, and administration. It's important to know how to administer SQL Database to fully benefit from all of the features and functionality that it provides. This book addresses important aspects of an Azure SQL Database instance such as migration, backup restorations, pricing policies, security, scalability, monitoring, performance optimization, high availability, and disaster recovery. It is a complete guide for database administrators, and ideal for those who are planning to migrate from on premise SQL Server database to an Azure SQL Server database. What you will learn Learn how to provision a new database or migrate an existing on-premise solution Understand how to backup, restore, secure, and scale your own Azure SQL Database Optimize the performance by monitoring and tuning your cloud-based SQL instance Implement high availability and disaster recovery procedures with SQL Database Develop a roadmap for your own scalable cloud solution with Azure SQL Database Who this book is for This book is ideal for database administrators, database developers, or application developers who are interested in developing or migrating existing applications with Azure SQL Database. Prior experience of working with an on-premise SQL Server deployment and brief knowledge of PowerShell and C# are recommended prerequisites.

Database management. --- SQL server.

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Find, explore, and extract big data to transform into actionable insights Key Features Perform end-to-end data analysis - from exploration to visualization Real-world examples, tasks, and interview queries to be a proficient data scientist Understand how SQL is used for big data processing using HiveQL and SparkSQL Book Description SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features. Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples. By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs. What you will learn Understand what data science is and how SQL Server is used for big data processing Analyze incoming data with SQL queries and visualizations Create, train, and evaluate predictive models Make predictions using trained models and establish regular retraining courses Incorporate data source querying into SQL Server Enhance built-in T-SQL capabilities using SQLCLR Visualize data with Reporting Services, Power View, and Power BI Transform data with R, Python, and Azure Who this book is for Hands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data scie...

Client/server computing. --- Database management. --- SQL server.

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Client/server computing. --- Database management. --- SQL server.

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Deliver advanced functionality faster and cheaper by exploiting SQL Server's ever-growing amount of built-in support for modern data formats. Learn about the growing support within SQL Server for operations and data transformations that have previously required third-party software and all the associated licensing and development costs. Benefit through a better understanding of what can be done inside the database engine with no additional costs or development time invested in outside software. Widely used types such as JSON and XML are well-supported by the database engine. The same is true of hierarchical data and even temporal data. Knowledge of these advanced types is crucial to unleashing the full power that's available from your organization's SQL Server database investment. SQL Server Advanced Data Types explores each of the complex data types supplied within SQL Server. Common usage scenarios for each complex data type are discussed, followed by a detailed discussion on how to work with each data type. Each chapter demystifies the complex data and you learn how to use the data types most efficiently. The book offers a practical guide to working with complex data, using real-world examples to demonstrate how each data type can be leveraged. Performance considerations are also discussed, including the implementation of special indexes such as XML indexes and spatial indexes. What You'll Learn: Understand the implementation of basic data types and why using the correct type is so important Work with XML data through the XML data type Construct XML data from relational result sets Store and manipulate JSON data using the JSON data type Model and analyze spatial data for geographic information systems Define hierarchies and query them efficiently through the HierarchyID type.

SQL server. --- Microsoft SQL server --- Database management. --- Database Management. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing

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Master SQL Server’s Concurrency Model so you can implement high-throughput systems that deliver transactional consistency to your application customers. This book explains how to troubleshoot and address blocking problems and deadlocks, and write code and design database schemas to minimize concurrency issues in the systems you develop. SQL Server’s Concurrency Model is one of the least understood parts of the SQL Server Database Engine. Almost every SQL Server system experiences hard-to-explain concurrency and blocking issues, and it can be extremely confusing to solve those issues without a base of knowledge in the internals of the Engine. While confusing from the outside, the SQL Server Concurrency Model is based on several well-defined principles that are covered in this book. Understanding the internals surrounding SQL Server’s Concurrency Model helps you build high-throughput systems in multi-user environments. This book guides you through the Concurrency Model and elaborates how SQL Server supports transactional consistency in the databases. The book covers all versions of SQL Server, including Microsoft Azure SQL Database, and it includes coverage of new technologies such as In-Memory OLTP and Columnstore Indexes. What You'll Learn: Know how transaction isolation levels affect locking behavior and concurrency Troubleshoot and address blocking issues and deadlocks Provide required data consistency while minimizing concurrency issues Design efficient transaction strategies that lead to scalable code Reduce concurrency problems through good schema design Understand concurrency models for In-Memory OLTP and Columnstore Indexes Reduce blocking during index maintenance, batch data load, and similar tasks.

SQL (Computer program language) --- Structured Query Language (Computer program language) --- Declarative programming languages --- Query languages (Computer science) --- SQL server. --- Microsoft SQL server --- Database management. --- Database Management. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing