Build a Real-Time Azure IoT Sensor Pipeline with Raspberry Pi Pico W + BME280

Why Use Azure IoT?

Azure IoT Hub is a fully managed service that lets you ingest, process, and route device telemetry at scale. Whether you're building a smart building, remote monitoring system, or industrial automation tool, Azure IoT is a powerful backbone for your solution.

But cloud platforms can be confusing. This tutorial gives you a real, fully working end-to-end setup using the Raspberry Pi Pico W and BME280 sensor, pushing live sensor data into Azure using HTTP and viewing it in Azure Data Explorer (ADX).

Components You’ll Need

• Raspberry Pi Pico W
• ShillehTek BME280 Sensor (Temp, Humidity, Pressure)
• Jumper Wires
• Breadboard (optional)
• Azure Account (free tier works)

What You'll Build

• Send sensor data from the Pico W to Azure IoT Hub using HTTP
• Ingest and store it in Azure Data Explorer
• Query the data using Kusto

Step 1: Create an Azure IoT Hub

1. Go to the Azure Portal
2. Search for IoT Hub → Click Create
3. Set:
   • Resource Group: raspberry-pi-group
   • Region: East US
   • Name: mypicowhub
   • 
4. Networking: Public access
5. Review + Create → Confirm and deploy

Give it time to deploy, can take a while.

Step 2: Register Your Device

1. Go to your IoT Hub → Devices → + Add Device
   1. 
2. Device ID: picow1
3. Auth Type: Symmetric Key → Enable auto-generate keys
   1. 
4. Click Save

Step 3: Create a SAS Token

In your terminal with Azure CLI installed, you can download the CLI with their official instructions, once you have the CLI you can run:

az iot hub generate-sas-token --hub-name mypicowhub --device-id picow1 --duration 3600

FYI make sure you sign into the Azure CLI first

This will give you the Authorization header to paste in your Pico code.

Step 4: Create Azure Data Explorer Cluster + Database

1. Search for Azure Data Explorer Clusters
2. Click + Create → Fill:
   • Cluster Name: picocluster
   • Workload is Dev/test

Once that is done you can add a database to the cluser:

Step 5: Create the Table + Mapping

Open your cluster → Query editor → Run:

.create table iotdata (
  deviceId: string,
  temperature: real,
  humidity: real,
  pressure: real,
  timestamp: datetime
)

Then create the JSON mapping:

.create table iotdata ingestion json mapping 'iotmap' '[{"column":"deviceId","path":"$.deviceId"},{"column":"temperature","path":"$.temperature"},{"column":"humidity","path":"$.humidity"},{"column":"timestamp","path":"$.timestamp"}]'

Step 6: Create the IoT Data Connection

1. Go to the picosensors database → Data connections → Add connection
2. Choose IoT Hub:
   • IoT Hub: mypicowhub
   • Table: iotdata
   • Format: JSON
   • Mapping: iotmap

All details are shown here (see image):

Step 7: Wire Up Your Pico W + BME280

• BME280 VCC → Pico 3.3V
• BME280 GND → Pico GND
• BME280 SDA → GP0
• BME280 SCL → GP1

Step 8: Final MicroPython Code

BME280 Library Code here:

Upload this to your Raspberry Pi Pico W:

import network
import time
import urequests
import json
from machine import Pin, I2C
import bme280

WIFI_SSID = 'SMA'
WIFI_PASS = 'mmmyellow'

IOTHUB_NAME = 'mypicowhub'
DEVICE_ID = 'picow1'
HEADERS = {
    'Authorization': 'SharedAccessSignature sr=mypicowhub.azure-devices.net%2Fdevices%2Fpicow1&sig=YOUR_SIG_HERE',
    'Content-Type': 'application/json'
}
URL = f'https://{IOTHUB_NAME}.azure-devices.net/devices/{DEVICE_ID}/messages/events?api-version=2020-09-30'

def connect_wifi():
    wlan = network.WLAN(network.STA_IF)
    wlan.active(True)
    wlan.connect(WIFI_SSID, WIFI_PASS)
    while not wlan.isconnected():
        print("Connecting...")
        time.sleep(1)
    print("WiFi connected:", wlan.ifconfig())

def get_iso_timestamp():
    t = time.localtime()
    return "{}-{:02d}-{:02d}T{:02d}:{:02d}:{:02d}Z".format(t[0], t[1], t[2], t[3], t[4], t[5])

i2c = I2C(0, sda=Pin(0), scl=Pin(1), freq=400000)
bme = bme280.BME280(i2c=i2c)

def send_to_azure():
    temp, pressure, humidity = bme.read_compensated_data()
    temp = round(temp / 100, 2)
    pressure = round(pressure / 25600, 2)
    humidity = round(humidity / 1024, 2)

    payload = {
        'deviceId': DEVICE_ID,
        'temperature': temp,
        'humidity': humidity,
        'pressure': pressure,
        'timestamp': get_iso_timestamp()
    }

    print("Sending:", payload)
    try:
        res = urequests.post(URL, headers=HEADERS, json=payload)
        print("Response:", res.status_code)
        res.close()
    except Exception as e:
        print("Error:", e)

connect_wifi()
while True:
    send_to_azure()
    time.sleep(1)

Step 9: Visualize the Data

Run this query in Azure Data Explorer:

iotdata
| where timestamp > ago(10m)
| order by timestamp desc

Or build a chart:

iotdata
| summarize avg(temperature) by bin(timestamp, 1m)
| render timechart

Conclusion

With just a few lines of MicroPython and a tiny sensor, you’ve built a real cloud pipeline using Azure IoT Hub and ADX. This setup is great for learning, prototyping, or even as a foundation for a scalable commercial project.

If you liked this tutorial, please consider subscribing on YouTube, supporting on BuyMeACoffee, or checking out the Raspberry Pi and Arduino components available at ShillehTek.