Use Jupyter notebooks
In this article, you will learn how to use Jupyter Notebook to analyse data persisted in the Quix platform
Why this is important
Although Quix is a realtime platform, to build realtime in-memory models and data processing pipelines, we need to understand data first. To do that, Quix offers a Data catalogue that makes data discovery and analysis so much easier.
You’ll need some data stored in the Quix platform. You can use any of our Data Sources available in the samples Library, or just follow the onboarding process when you sign-up to Quix.
You will also need Python 3 environment set up in your local environment.
Install Jupyter notebooks as directed here.
Create a new notebook file
You can now run jupyter from the Windows start menu or with the following command in an Anaconda Powershell Prompt, or the equivalent for your operating system.
Then create a new Python3 notebook
Connecting Jupyter notebook to Data Catalogue
The Quix web application has a python code generator to help you connect your Jupyter notebook with Quix.
You need to be logged into the platform for this:
Select workspace (you likely only have one)
Go to the Data Explorer
Add a query to visualize some data. Select parameters, events, aggregation and time range
Select the Code tab
Ensure Python is the selected language
Copy the Python code to your Jupyter notebook and execute.
If you want to use this generated code for a long time, replace the temporary token with PAT token. See authenticate your requests how to do that.
Too much data
If you find that the query results in more data than can be handled by Jupyter Notebooks try using the aggregation feature to reduce the amount of data returned.
For more info on aggregation check out this short video.