100% Free Forever
AI-Powered Learning
Industry Expert Content
Certificates & Badges
Learn At Your Own Pace
Database

Microsoft SQL Server

By Microsoft

IntermediateTool7.3K learners

Microsoft SQL Server is a relational database management system (RDBMS) developed by Microsoft that stores and retrieves data using Transact-SQL (T-SQL), an extended dialect of SQL, and is widely used for enterprise transaction processing,…

Definition

Microsoft SQL Server is a relational database management system (RDBMS) developed by Microsoft that stores and retrieves data using Transact-SQL (T-SQL), an extended dialect of SQL, and is widely used for enterprise transaction processing, business intelligence, and analytics.

Overview

SQL Server traces back to a partnership with Sybase in the late 1980s before Microsoft took the product in its own direction for Windows in the 1990s. It has since grown into a full data platform spanning the core relational engine, reporting and analysis services, integration tooling, and machine learning integration. Like other relational database systems, SQL Server organizes data into tables with defined schemas and enforces ACID Properties for transactional integrity. It supports both OLTP workloads through row-store tables and OLAP-style analytics through columnstore indexes, and it competes directly with PostgreSQL, MySQL, and IBM Db2 in the enterprise database market. Microsoft ships SQL Server in on-premises editions (Express, Standard, Enterprise) as well as a fully managed cloud version, Azure SQL Database, which shares the same query engine but removes most infrastructure management. Organizations building on Microsoft's data stack often pair SQL Server administration skills with broader cloud training such as the AWS Solutions Architect course when running hybrid or multi-cloud environments.

Key Features

  • Transact-SQL (T-SQL) procedural extension of standard SQL
  • Row-store and columnstore indexes for mixed OLTP/OLAP workloads
  • Always On Availability Groups for high availability and disaster recovery
  • In-database machine learning via SQL Server Machine Learning Services
  • Integration Services (SSIS), Analysis Services (SSAS), and Reporting Services (SSRS)
  • Transparent data encryption and row-level security for compliance
  • In-memory OLTP engine for high-throughput transactional tables
  • Tight integration with the Windows Server and .NET ecosystem

Use Cases

Enterprise transaction processing for ERP and CRM systems
Business intelligence and reporting via SSRS and Power BI
Data warehousing with columnstore-backed analytical queries
Line-of-business application backends built on .NET
Hybrid cloud deployments migrating toward Azure SQL Database
Financial systems requiring strict ACID transaction guarantees

Frequently Asked Questions

From the Blog