Creating Custom Interactive Dashboards with Bokeh and BigQuery
In this lab you will learn how to build a custom interactive dashboard application on Google Cloud Platform (GCP) by using the Bokeh library to visualize data from publicly available Google BigQuery datasets. You will also learn how to deploy this application with both security and scalability in mind.
Powerful software-as-a-service (SaaS) solutions like Data Studio are extremely useful when building interactive data visualization dashboards. However, these types of solutions might not provide sufficient customization options. For those scenarios, you can use open source libraries like D3.js, Chart.js, or Bokeh to create custom dashboards. While these libraries offer a lot of flexibility for building dashboards with tailored features and visualizations, a major challenge remains: deploying these dashboards to the internet to enable easy stakeholder access.
- Learn how to connect to BigQuery from a custom application built using Bokeh.
- Implement optimization techniques, including parallel queries and application-level caching using Memcached.
- Deploy the dashboard demo to Google Kubernetes Engine using Kubernetes.
- Create an HTTPS Load Balancer.
- Deploy a CDN-enabled backend for serving static assets.
- Secure the application by using Cloud Identity-Aware Proxy authentication and SSL encryption.
Before you click the Start Lab button
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.
This Qwiklabs hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.
What you need
To complete this lab, you need:
- Access to a standard internet browser (Chrome browser recommended).
- Time to complete the lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab.
Note: If you are using a Pixelbook please open an Incognito window to run this lab.
How to start your lab and sign in to the Google Cloud Console
Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is a panel populated with the temporary credentials that you must use for this lab.
Copy the username, and then click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Open the tabs in separate windows, side-by-side.
In the Sign in page, paste the username that you copied from the Connection Details panel. Then copy and paste the password.
Important: You must use the credentials from the Connection Details panel. Do not use your Qwiklabs credentials. If you have your own Google Cloud account, do not use it for this lab (avoids incurring charges).
Click through the subsequent pages:
- Accept the terms and conditions.
- Do not add recovery options or two-factor authentication (because this is a temporary account).
- Do not sign up for free trials.
After a few moments, the Cloud Console opens in this tab.
Activate Cloud Shell
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.
It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. For example:
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
You can list the active account name with this command:
gcloud auth list
Credentialed accounts: - <myaccount>@<mydomain>.com (active)
Credentialed accounts: - firstname.lastname@example.org
You can list the project ID with this command:
gcloud config list project
[core] project = <project_ID>
[core] project = qwiklabs-gcp-44776a13dea667a6
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