Microsoft SQL Server

Microsoft SQL Server 

Microsoft SQL Server is a relational database management system, or RDBMS, that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. It's one of the three market-leading database technologies, along with Oracle Database and IBM's DB2.

Like other RDBMS software, Microsoft SQL Server is built on top of SQL, a standardized programming language that database administrators (DBAs) and other IT professionals use to manage databases and query the data they contain. SQL Server is tied to Transact-SQL (T-SQL), an implementation of SQL from Microsoft that adds a set of proprietary programming extensions to the standard language.

Microsoft SQL Server 2016, which became generally available in June 2016, was developed as part of a "mobile first, cloud first" technology strategy adopted by Microsoft two years earlier. Among other things, SQL Server 2016 added new features for performance tuning, real-time operational analytics, and data visualization and reporting on mobile devices, plus hybrid cloud support that lets DBAs run databases on a combination of on-premises systems and public cloud services to reduce IT costs. For example, a SQL Server Stretch Database technology moves infrequently accessed data from on-premises storage devices to the Microsoft Azure cloud, while keeping the data available for querying, if needed.

SQL Server 2016 also increased support for big data analytics and other advanced analytics applications through SQL Server R Services, which enables the DBMS to run analytics applications written in the open source R programming language, and PolyBase, a technology that lets SQL Server users access data stored in Hadoop clusters or Azure blob storage for analysis. In addition, SQL Server 2016 was the first version of the DBMS to run exclusively on 64-bit servers based on x64 microprocessors. And it added the ability to run SQL Server in Docker containers, a virtualization technology that isolates applications from each other on a shared operating system.

Microsoft SQL Server ran exclusively on Windows for more than 20 years. But, in 2016, Microsoft said it planned to also make the DBMS available on Linux, starting with a new version released as a community technology preview that November and initially dubbed SQL Server vNext; later, the update was formally named SQL Server 2017, and it became generally available in October of that year.

The support for running SQL Server on Linux moved the database platform onto an open source operating system commonly found in enterprises, giving Microsoft potential inroads with customers that don't use Windows or have mixed server environments. SQL Server 2017 also expanded the Docker support added for Windows systems in the previous release to include Linux-based containers.

Another notable feature in SQL Server 2017 is support for the Python programming language, an open source language that is widely used in analytics applications. With its addition, SQL Server R Services was renamed Machine Learning Services (In-Database) and expanded to run both R and Python applications. Initially, the machine learning toolkit and a variety of other features are only available in the Windows version of the database software, with a more limited feature set supported on Linux.

Inside SQL Server's architecture

Like other RDBMS technologies, SQL Server is primarily built around a row-based table structure that connects related data elements in different tables to one another, avoiding the need to redundantly store data in multiple places within a database. The relational model also provides referential integrity and other integrity constraints to maintain data accuracy; those checks are part of a broader adherence to the principles of atomicity, consistency, isolation and durability -- collectively known as the ACID properties and designed to guarantee that database transactions are processed reliably.

Sitting beneath the Database Engine is the SQL Server Operating System or SQLOS; it handles lower-level functions, such as memory and I/O management, job scheduling and locking of data to avoid conflicting updates. A network interface layer sits above the Database Engine and uses Microsoft's Tabular Data Stream protocol to facilitate request and response interactions with database servers. And at the user level, SQL Server DBAs and developers write T-SQL statements to build and modify database structures, manipulate data, implement security protections and back up databases, among other tasks.

SQL Server services, tools, and editions

Microsoft also bundles a variety of data management, business intelligence (BI) and analytics tools with SQL Server. In addition to the R Services and now Machine Learning Services technology that first appeared in SQL Server 2016, the data analysis offerings include SQL Server Analysis Services, an analytical engine that processes data for use in BI and data visualization applications, and SQL Server Reporting Services, which supports the creation and delivery of BI reports.

On the data management side, Microsoft SQL Server includes SQL Server Integration Services, SQL Server Data Quality Services, and SQL Server Master Data Services. Also bundled with the DBMS are two sets of tools for DBAs and developers: SQL Server Data Tools, for use in developing databases, and SQL Server Management Studio, for use in deploying, monitoring and managing databases.

Most of those tools and the other features in Microsoft SQL Server are also supported in Azure SQL Database, a cloud database service built on the SQL Server Database Engine. Alternatively, users can run SQL Server directly on Azure, via a technology called SQL Server on Azure Virtual Machines; it configures the DBMS in Windows Server virtual machines running on Azure. The VM offering is optimized for migrating or extending on-premises SQL Server applications to the cloud, while Azure SQL Database is designed for use in new cloud-based applications.

In the cloud, Microsoft also offers Azure SQL Data Warehouse, a data warehousing service based on massively parallel processing (MPP) implementation of SQL Server. The MPP version, originally a stand-alone product called SQL Server Parallel Data Warehouse, is also available for on-premises uses as part of the Microsoft Analytics Platform System, which combines it with PolyBase and other big data technologies.

자료 출처

https://searchsqlserver.techtarget.com/definition/SQL-Server

https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/tabular-models-ssas?view=sql-server-2017

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