Training Data Versioning with Azure SQL Database
PythonWhen setting up a training data versioning system with Azure SQL Database, you would typically want to create a managed SQL database instance where your training data can be stored and versioned. Azure SQL Database is a managed database service which means that Microsoft Azure handles most of the database management functions like upgrading, patching, backups, and monitoring without user involvement.
In the context of training data versioning, Azure SQL Database allows you to keep track of different versions of your datasets and models. It provides capabilities such as point-in-time restore, long-term backup retention, and automated backups that can be helpful for versioning and maintaining your training data.
Here is a Pulumi program that demonstrates how you can create an Azure SQL server along with a managed database. The program uses the
azure-native
SDK, which gives you fine-grained control over the Azure resources you are creating or managing.Explanation of Resources Being Used
- Resource Group: Azure resource groups are containers that hold related resources for an Azure solution. In this case, it holds our SQL server and database.
- SQL Server: This represents the SQL server that will host our managed database.
- Managed Database: A managed database is a database within the SQL server. It's the database that you will interact with to store and query your training data.
The Pulumi Program
I'll guide you through creating these resources using Pulumi's Python SDK.
import pulumi import pulumi_azure_native.sql as sql import pulumi_azure_native.resources as resources # Create an Azure Resource Group resource_group = resources.ResourceGroup('resource_group') # Create an Azure SQL Server sql_server = sql.Server('sqlServer', resource_group_name=resource_group.name, location=resource_group.location, version="12.0", # For example, use "12.0" string for "SQL Server 2014" or "140" for "SQL Server 2017" administrator_login='pulumiadmin', administrator_login_password='ComplexPassword9012#', public_network_access='Enabled' ) # Create a Managed SQL Database in the SQL Server managed_db = sql.ManagedDatabase('managedDatabase', resource_group_name=resource_group.name, managed_instance_name=sql_server.name, location=sql_server.location, collation='SQL_Latin1_General_CP1_CI_AS' ) # Export the connection string for the SQL Database primary_connection_string = pulumi.Output.all(sql_server.name, resource_group.name, managed_db.name).apply( lambda args: f"Server=tcp:{args[0]}.database.windows.net;Initial Catalog={args[2]};Persist Security Info=False;User ID={sql_server.administrator_login};Password={sql_server.administrator_login_password};MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;" ) pulumi.export('primary_sql_connection_string', primary_connection_string)
In this program, we have:
- Created a new resource group.
- Deployed a new Azure SQL server to the resource group.
- Created a managed database inside the SQL server.
- Generated and exported a primary connection string for the SQL database which you can use to connect to your database from your application or data processing tools.
This sets up the fundamental architecture on Azure for managing your training data. You can then use SQL commands and Azure's features to manage data versions, take backups, and restore to previous points as needed. The database can be further configured for specific versioning requirements, but those details would be handled at the database level with SQL queries and stored procedures or with additional Azure services tailored to your data management needs.
Remember, handling sensitive data like the administrator login and password in this example should be done securely, such as by using the Pulumi Config system or an Azure Key Vault service, instead of hardcoding values like the above. This is just a demonstration of how you can setup your managed database with Pulumi.