Microsoft Azure SQL Database
Build apps that scale with managed and intelligent SQL database in the cloud.
Integrate the Microsoft Azure SQL Database API with the Python API
Setup the Microsoft Azure SQL Database API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Microsoft Azure SQL Database and Python remarkably fast. Free for developers.
Popular Microsoft Azure SQL Database and Python Actions#
Executes a SQL query and returns the results. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Inserts a new row in a table. See the documentation
Overview of Microsoft Azure SQL Database#
The Microsoft Azure SQL Database API allows you to manage and interact with your SQL databases hosted on Azure directly from Pipedream. It provides a serverless way to run SQL queries, manage tables, and handle database management tasks. You can create, read, update, and delete database records, execute stored procedures, and perform a variety of other SQL operations. Leveraging this API on Pipedream enables you to automate workflows, respond to database events in real-time, and integrate with countless other apps and services.
Connect Microsoft Azure SQL Database#
import sql from "mssql"
export default defineComponent({
props: {
azure_sql: {
type: "app",
app: "azure_sql",
}
},
async run({steps, $}) {
const { host, username, password, port, database, encrypt } = this.azure_sql.$auth
const config = {
user: username,
password,
server: host,
port,
database,
authentication: {
type: 'default'
},
options: {
encrypt
}
}
await sql.connect(config);
return await sql.query`SELECT GETDATE()`
},
})
Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}