#039 How to create a SQL Server project via Docker in Azure Data Studio

Nesse vídeo apresentamos como criar Projeto de Banco de Dados em instância SQL Server via Container do Docker utilizando o Azure Data Studio.

We'll get to know the techniques:

1. What is Azure Data Studio?

Azure Data Studio is a lightweight, cross-platform database development environment provided by Microsoft. It supports multiple database management systems (DBMS), including SQL Server, PostgreSQL, MySQL, and others. Azure Data Studio is designed for developers, analysts, and database administrators.

2. Install SQL Server

extensions:

In Azure Data Studio, you can extend its capabilities by installing extensions. SQL Server extensions can provide additional functionality, such as support for advanced languages, graphics, and queries. Extensions can be installed from the "Extensions" tab of Azure Data Studio.

3. Create a SQL Server instance via container (Docker, Script, Python): Docker

is often used to create and manage SQL Server containers. One can use scripts or even tools like Python to automate the creation of SQL Server containers.

4. Create the database project (SQL Database Projects):

A database project in Azure Data Studio is an organized structure that contains SQL scripts, database objects, and other artifacts related to database development. These projects can be managed in Azure Data Studio and provide version control and comparison tools.

5. Publish the Database Project:

Publishing a database project to

Azure Data Studio involves implementing the changes defined in the project to a target database. This allows changes to the schema or data to be applied in a controlled manner.

6. Import File in CSV Format (Flat File Wizard):

Flat File Wizard is a tool that allows you to import data from CSV files or other flat formats into SQL Server tables. It guides the user through the process of mapping columns and setting import options.

7. Compare Database Schema (Schema Compare):

The Schema Compare functionality allows you to compare two SQL Server databases or a database project with a real database. This is useful for identifying differences in the schema and managing synchronization between them.