What is the best way to access data in the data bricks by using azure function?

Question

I just tried to load data from data bricks by using data bricks jobs API and azure function.

Can I know is there another way to do the same thing that based on azure function?

Answer 1

Generate a Databricks personal access token, then :

import requests

import json

import os

def main(req):

    # Set your Databricks instance URL and token

    databricks_instance = 'https://<your-databricks-instance>'

    databricks_token = os.getenv('DATABRICKS_TOKEN')

    # Define the endpoint for the Databricks REST API

    endpoint = f'{databricks_instance}/api/2.0/jobs/run-now'

    # Define the job parameters

    job_id = '<your-job-id>'

    headers = {

        'Authorization': f'Bearer {databricks_token}',

        'Content-Type': 'application/json'

    }

    payload = {

        'job_id': job_id,

        'notebook_params': {

            'param1': 'value1'

        }

    }

    # Make the request to the Databricks API

    response = requests.post(endpoint, headers=headers, data=json.dumps(payload))

    # Check the response status

    if response.status_code == 200:

        return response.json()

    else:

        return f'Error: {response.status_code}, {response.text}'

Another way also, you can connect to Databricks SQL Analytics endpoints using JDBC or ODBC drivers and you need to Install pyodbc or other JDBC/ODBC libraries in your Azure Function.

import pyodbc

def main(req):

    server = 'your-databricks-server'

    database = 'your-database'

    username = 'your-username'

    password = 'your-password'

    driver = '{ODBC Driver 17 for SQL Server}'

    connection_string = f'DRIVER={driver};SERVER={server};PORT=1433;DATABASE={database};UID={username};PWD={password}'

    with pyodbc.connect(connection_string) as conn:

        cursor = conn.cursor()

        cursor.execute("SELECT * FROM your_table")

        rows = cursor.fetchall()

        return [dict(zip([column[0] for column in cursor.description], row)) for row in rows]

You can integrate Azure Functions with Azure Event Hub or ADF to trigger data movement or processing tasks in Databricks. For instance, an Azure Function can listen to events in Event Hub and trigger corresponding Databricks jobs.

If you prefer command-line tools, you can invoke Databricks CLI commands from your Azure Function using subprocess :

import subprocess

def main(req):

    databricks_token = os.getenv('DATABRICKS_TOKEN')

    databricks_instance = 'https://<your-databricks-instance>'

    command = [

        'databricks', '--token', databricks_token,

        '--host', databricks_instance,

        'jobs', 'run-now', '--job-id', '<your-job-id>'

    ]

    result = subprocess.run(command, capture_output=True, text=True)

    if result.returncode == 0:

        return result.stdout

    else:

        return f'Error: {result.stderr}'

Each of these methods has its own use cases and benefits. The best approach depends on your specific requirements, such as the nature of the data processing, security considerations, and how you want to manage and scale your Azure Functions.

Answer 2

Hi there Athula Chandrawansha

Thanks for using QandA platform

I guess the best way to access data in Databricks using Azure Functions, aside from using the Databricks Jobs API, is to query data directly from Databricks SQL endpoints. Set up a Databricks SQL endpoint and use Azure Functions to connect to it via libraries like pyodbc or sqlalchemy to execute SQL queries. Alternatively, Azure Functions can directly read from ADLS for real-time data processing.

If this helps kindly accept the answer thanks much.