Overview
Azure SQL Database is a fully managed cloud relational database service built on the SQL Server engine, published by Microsoft as part of the Azure cloud platform. It handles patching, backups, high availability, and infrastructure management automatically. Project teams use it as the operational record of truth for transactional data, including orders, accounts, customer records, compliance documents, and more. It includes service tiers from General Purpose through Business Critical and Hyperscale (up to 128 TB), a 99.99% availability SLA, and native AI capabilities including vector indexing and outbound REST calls from T-SQL via sp_invoke_external_rest_endpoint to approved Azure services.
Datagrid connects to Azure SQL Database to read tables, query results, and stored procedure outputs, then applies agentic AI processing to that data. Datagrid's AI agents extract records, enrich them with external data or LLM inference, and write results back to Azure SQL or route them to downstream systems like HubSpot CRM, Jira, or Intercom. This integration covers direct SQL connectivity, queries, stored procedures, and table write-back through Datagrid workflows.
Common data flows between the two systems include pulling structured records into Datagrid for agentic AI transformation, syncing operational data with SaaS tools, triggering workflows from database events, and writing enriched data back to Azure SQL columns for downstream analytics and segmentation.
How to integrate Azure SQL Database with Datagrid
Datagrid connects directly to your Azure SQL Database so AI agents can read tables, run queries, execute stored procedures, and write processed results back to target tables. Setup focuses on entering the right connection details, authenticating with SQL credentials, and confirming network access to the database.
Connect your Azure SQL Database
The Azure SQL Database integration gives Datagrid read and write access to Azure SQL tables, query results, and stored procedures. It uses SQL authentication with a username and password to establish a direct connection to your database.
Sign in to your Datagrid account. In the left panel of your Workflow, click Integrations.
Under the Databases header, find the Azure SQL thumbnail and click Setup Integration.
Click Create a Connection.
Fill in the required credential fields:
Configuration Name: a descriptive label (e.g.,
Azure-SQL-production-west)Host: your Azure SQL server's publicly routable hostname
Port: the SQL Server port (default: 1433)
Database: the exact database name
Azure SQL Username
Azure SQL Password
Click Validate Configuration. Valid fields show green; invalid fields highlight in red.
Click Add Configuration to finalize the connection.
A typical configuration looks like this:
configuration_name: Azure-SQL-production-west
host: your-server.database.windows.net
port: 1433
database: your_database_name
username: your_sql_username
password: your_sql_password
authentication: sql
Authenticate with SQL credentials
The Datagrid integration uses SQL authentication, which means a username and password pair with database-level access. Your Azure SQL server must also allow connections from Datagrid's IP addresses. Verify that:
The host is publicly routable
TCP is enabled on the correct port
The database name, username, and password are correct
Datagrid IPs are whitelisted in your Azure SQL server-level firewall rules
Firewall rule changes can take up to 5 minutes to propagate.
Configure data sync behavior
The integration can read from Azure SQL tables, views (via query), custom SQL query results, and stored procedure result sets. Write operations target tables directly. Sync direction depends on your workflow configuration. Datagrid's AI agents can pull data for processing, push enriched results back, or do both. Sync frequency is controlled by your workflow triggers and scheduling within Datagrid.
For deeper setup details, use the Datagrid setup guide linked at the top of this page.
Why use Azure SQL Database with Datagrid
Azure SQL Database often holds the operational data that teams need to act on quickly. Datagrid connects that data to execution so project teams can process records, enrich them, and route the results without manual exports.
Agentic data extraction from live SQL tables: Datagrid's AI agents query Azure SQL records directly, with no manual CSV exports or scheduled report downloads required.
Automated record enrichment: Datagrid's AI agents read rows from Azure SQL, enrich each record with classifications, sentiment scores, or firmographic data from external sources, and write results back to designated columns.
Cross-platform sync without custom middleware: Operational data in Azure SQL stays synchronized with SaaS tools through Datagrid's integration network, with AI logic determining field mappings and conflict resolution.
Event-driven workflow triggers: A new row or record change in Azure SQL can trigger a workflow that includes retrieval, LLM inference, downstream routing, and audit logging.
Structured + unstructured data processing: Datagrid's AI agents combine Azure SQL's structured relational data with unstructured inputs from file storage and similar sources to produce complete, context-rich outputs.
Write-back closes the loop: Datagrid's AI agents write processed results back to Azure SQL, keeping your operational database current with AI-generated insights.
What you can build with Azure SQL Database and Datagrid
Azure SQL Database works well as the system of record in workflows that need extraction, enrichment, routing, and write-back. The examples below show how Datagrid's AI agents execute that work across operational systems.
Automated CRM sync from transactional data: Azure SQL holds your order history, account records, and transaction logs. Datagrid's AI agents extract incremental changes, normalize field formats, and push updates to your CRM so sales teams see current order data without running manual reports or waiting on batch jobs.
AI-enriched customer records for segmentation: Datagrid reads a table of company or contact records from Azure SQL, runs each through LLM-based classification (industry, sentiment, intent scoring), and writes enrichment data back to new columns in the same table. Downstream analytics queries and marketing automation workflows consume the enriched data directly.
Agentic document processing triggered by database events: When a new row appears in an Azure SQL table, such as a flagged support ticket, a new compliance filing, or a submitted RFI, a Datagrid agent retrieves related records from additional tables, assembles context, runs LLM inference for classification or response drafting, and routes the output to communication tools or writes an audit entry back to Azure SQL.
Cross-system reporting with AI-generated summaries: Datagrid's AI agents pull financial, operational, or project data from Azure SQL on a schedule, compare current values against historical baselines, and generate narrative summaries with flagged anomalies. Reports are distributed via email or pushed to project management tools as formatted updates.
These patterns keep operational data in Azure SQL while Datagrid executes the work around it.
Resources and documentation
Use the references below for product details, connectivity requirements, and troubleshooting:
Datagrid connectors index - full list of available Datagrid integrations
Azure SQL Database PaaS overview - overview of Azure SQL Database as a fully managed service
Azure SQL Database firewall configuration - IP whitelisting and firewall rule setup
Azure SQL Database connectivity architecture - port requirements and connection policies
Azure SQL Database connect and query reference guide - language-specific quickstarts for .NET, Python, Node.js, Java, Go, PHP, Ruby, and sqlcmd
Azure SQL Database troubleshoot common connection errors - resolution steps for frequent connectivity issues
Frequently asked questions
What authentication method does the Datagrid Azure SQL Database connector use?
The Datagrid integration uses SQL authentication, with a username and password entered during setup. No OAuth or token-based authentication is documented for this integration. Azure SQL Database itself includes additional methods including Microsoft Entra ID and Managed Identity, but the Datagrid integration configuration currently requires the SQL username and password fields.
What firewall configuration is required to connect Datagrid to Azure SQL Database?
Public access is controlled by server-level firewall rules. You must add Datagrid's IP addresses to your server-level firewall rules. The database requires TCP port 1433 for external (Proxy mode) connections. Firewall changes can take up to 5 minutes to propagate.
What are the most common reasons the connection fails?
Common failure points include a wrong host or IP, Datagrid IPs not added to the firewall allow list, an incorrect port, TCP not enabled, and an invalid database name. If the connection authenticates but cannot access data, verify the database name and that the account has database-level access. Additional error scenarios are covered in the Azure SQL Database troubleshooting guide listed in the Resources and documentation section.
Does Azure SQL Database support Change Data Capture for incremental sync?
Yes. Azure SQL Database supports Change Data Capture (CDC), which tracks the full history of INSERT, UPDATE, and DELETE operations with old and new values. CDC must be enabled at the database level by running sys.sp_cdc_enable_db, then enabled individually per table. Setup and verification steps are covered in the CDC overview for Azure SQL Database.
What TLS version is required for connections to Azure SQL Database?
Azure SQL Database requires TLS 1.2 or higher. Verify that all client tools and drivers support TLS 1.2 before configuring the integration.
Similar integrations
HubSpot CRM: CRM integration commonly paired with Azure SQL Database to sync transactional records with sales and marketing workflows.
Jira: Project management integration for routing Azure SQL-triggered events into task tracking and operational workflows.