In the previous post I outlined connecting our temperature/humidity sensor to a Raspberry Pi and taking a reading from it. The next step is to send that data to the cloud and make use of it in Power BI.

Here’s a basic outline of the process

Connecting the sensor to the Cloud

Of course, Power BI isn’t the only thing you could use this data for, though our goal is to add this temperature reading to the Power BI dashboard in our office.

The first thing I needed to find was somebody who had done this before. A few days ago I found this post by Rakesh George that outlines his process:

His temperature sensor setup is a lot more complicated than mine – it looks like he’s taking the binary values from the sensor and working out the temperature value. In our case, we had a great library from Adafruit that did all of that for us.

Compared to other IoT projects, this one is extremely simple. We’re not making use of Event Hubs or Stream Analytics or any of the high scale, high performance tools in Azure. We’re just sending a temperature and humidity value to Azure Table Storage once a minute, then connecting to that data via Power BI Desktop.

I downloaded Rakesh’s source files to see how it can apply to my setup.

Firstly, you’ll need to install the Azure SDK on the Raspberry Pi to make use of the Azure services, check the linuxruninstructions.txt in Rakesh’s Python source file download for instructions on this.

Most of the code relevant to Azure is located in the file. I learnt a lot about the Azure Python SDK by reading and modifying this script file.

If you’re setting this up yourself, you’ll need to sign up for Microsoft Azure (free trials are available), create a storage account, and add the storage account name and primary key into the sample code.

After a little bit of tweaking, I was able to get it to work using the Adafruit library to retrieve the values from my sensor and upload it to my Azure Table Storage account every 60 seconds.

Once you’re collecting data, you can use the Power BI Desktop App to pull this data into your dashboards for reporting. As I’m testing it, I created a new dashboard for these temperature/humidity values.

Power BI Desktop can be downloaded from here:

A basic version of Power BI is available free, though you can sign up for a 60 day trial of Power BI Pro, which has many of the same features, though it refreshes your dashboards hourly instead of daily.

Importing and preparing the data for Power BI

Click Get Data, choose Azure, Microsoft Azure Table Storage.

Get Data in Power BI

Enter the storage account name, click OK, then enter the primary key.

Connect to Azure Table Storage

Select the table to import.

Connect to data in Power BI

Import the data, and use the query editor to set the data types and separate the columns for humidity and temperature values.

Modify Data in Power BI

Create your Power BI graphs and publish them to Power BI.

Building Graphs in Power BI

Once published, you can access them via the browser or the Power BI apps on mobile devices.

View graphs on Power BI Mobile Devices

At GCITS we’re getting familiar with the Internet of Things (IoT). To start with, we’re connecting sensors to the cloud to make use of the real time data.

As a quick test, we want to connect a temperature sensor in our office to our dashboard in Power BI. The first step is to get a reading from the temperature sensor via the Raspberry Pi.

I had a bit of trouble getting this working using the some of the suggested wiring diagrams (it may just be an issue with my breadboard) so I’m posting this here in case it helps someone else.

You will need:

  • Raspberry Pi running Raspbian
  • A Breadboard
  • Assorted wires
  • 10k ohm resistor
  • DHT11 Temperature/Humidity Sensor

Wire it all up

First step is to wire up your Raspberry Pi to the DHT11 temperature sensor.

Here’s a drawing of my wiring, as well as two photos that should make it clear.

DHT11 Wiring Diagram
Raspberry Pi Wiring
DHT11 Wiring On Breadboard


Set up your Raspberry Pi

Once you’re all set, you’ll need to install a library to easily pull data from the sensor. I’m getting started with Python, so I’m using the Python library provided by Adafruit. I found some great instructions in this PDF:

I’ll summarise it here:

Open the terminal on your Raspberry Pi and run the following commands:

git clone
cd Adafruit_Python_DHT

This will clone the Adafruit Python Library to your Pi

To make sure you have the correct dependencies to use the library, you’ll also need to run these commands:

sudo apt-get update
sudo apt-get install build-essential python-dev python-openssl

Next you’ll need to install the library we cloned earlier:

sudo python install

To confirm that you’ve successfully installed the library, test the sensor by navigating to the Examples folder and running the test python script:

cd examples
sudo ./ 11 4

This tests pin GPIO 4 for the DHT11 sensor and returns the temperature and humidity values.
Temperature and Humidity Results
Temperature Outside


As you can see, the result seems to be pretty accurate.

The parameters at the end can be modified to suit your setup. If you’re using the DHT22 sensor, replace 11 with 22 , or if you’re using another GPIO pin, substitute 4 for the appropriate GPIO pin number.

For example, sudo ./ 22 17 refers to a DHT22 on GPIO17

Stay tuned for future posts on how we’ll connect this data to the cloud and Power BI!

This week we’re exploring the capabilities of Microsoft Power BI (an Office 365 add-on) to give us a clear picture of our daily performance.

Power BI allows you to connect multiple data sources from a wide range of on-premise and cloud services and view the live data in a clean dashboard. You can view and share dashboards from the browser, the windows app, or the mobile apps for iOS, Android or Windows Phone. In our case, we wanted the screen in our office to focus on a couple of things: the performance of our support team, and our website statistics.

We’re tracking the support team performance because it’s the core of our business, and the website performance because we’re focusing on delivering more useful content and would like to see how it’s received.

The data we need to track these metrics is stored in external silos – Zendesk and Google Analytics. Luckily, Power BI makes it easy to connect these data sources to a single dashboard.

Here’s a video of it starting up, and a photo of the finished dashboard:

Power BI Dashboard

This setup uses a Raspberry Pi connected to an Azure Virtual Machine running Power BI through the browser.

We’ll be adding new features in the next few weeks  involving additional Raspberry Pis, some connected sensors and Azure SQL. Stay tuned!