SQL developer tools
Azure Data Studio
Azure Data Studio is a cross-platform database tool for data professionals using the Microsoft family of on-premises and cloud data platforms on Windows, MacOS, and Linux.
Previously released under the preview name SQL Operations Studio, Azure Data Studio offers a modern editor experience with IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built in charting of query result sets and customizable dashboards.
SQL extension for Visual Studio Code
Visual Studio Code is a cross-platform, graphical code editor that supports extensions. You can use Visual Studio Code to create and run Transact-SQL scripts.
SQL Server Data Tools
SQL Server Data Tools (SSDT) transforms database development by introducing a ubiquitous, declarative model that spans all the phases of database development inside Visual Studio. You can use SSDT Transact-SQL design capabilities to build, debug, maintain, and refactor databases. You can work with a database project, or directly with a connected database instance on or off-premise.
Developers can use familiar Visual Studio tools for database development. Tools such as: code navigation, IntelliSense, language support that parallels what is available for C# and Visual Basic, platform-specific validation, debugging, and declarative editing in the Transact-SQL editor. SSDT also provides a visual Table Designer for creating and editing tables in either database projects or connected database instances. While you are working on your database projects in a team-based environment, you can use version control for all the files. When it’s time to publish your project, you can publish to all supported SQL platforms; including SQL Database and SQL Server. SSDT platform validation capability ensures that your scripts work on the target you specify.
Data loading and migration
The bcp utility (Bcp.exe) is a command-line tool that uses the Bulk Copy Program (BCP) API. The bcp utility performs the following tasks:
- Bulk exports data from a SQL Server table into a data file.
- Bulk exports data from a query.
- Bulk imports data from a data file into a SQL Server table.
- Generates format files.
The bcp utility is accessed by the bcp command. To use the bcp command to bulk import data, you must understand the schema of the table and the data types of its columns, unless you are using a pre-existing format file.
SQL Server Migration Assistant
Microsoft SQL Server Migration Assistant (SSMA) is a tool designed to automate database migration to SQL Server from Microsoft Access, DB2, MySQL, Oracle, and SAP ASE.
SQL Server Integration Services (SSIS)
Microsoft Integration Services is a platform for building enterprise-level data integration and data transformations solutions. Use Integration Services to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data.
Integration Services can extract and transform data from a wide variety of sources such as XML data files, flat files, and relational data sources, and then load the data into one or more destinations.
Integration Services includes a rich set of built-in tasks and transformations, graphical tools for building packages, and the Integration Services Catalog database, where you store, run, and manage packages.
Data science tools and services
Easily create your predictive models with Visual Studio using the Python Tools, R Tools, or F#, and run them as an Azure web service or by using SQL Server R Services.
- Python tools – Editing, debugging, interactive development for Python apps, using familiar frameworks including Django and Flask
- R tools – You can run your code directly in the Interactive window, or ctrl-enter from the Editor, or the History window to quickly see computation results or graphs. Up arrow to edit single lines or multiple-line chunks of code, as if you’re in the editor.
- F# – F# is well-suited to machine learning because of its efficient execution, succinct style, data access capabilities and scalability. F# has been successfully used by some of the most advanced machine learning teams in the world, including several groups at Microsoft Research.