Real-time Data Visualization with Python and Google Data Studio: How to Use Python to Fetch Real-time Data and Create Live Dashboards in Google Data Studio

Marketing experts, data scientists, and business analysts worldwide require tools to communicate complex data, key performance indicators, and results to non-technical users. They need the right data visualization and reporting platforms to produce and share insightful and interactive dashboards.

For businesses seeking tools to learn and create real-time reports, Google Data Studio is the perfect solution. The cutting-edge software allows you to test, visualize, and create stunning reports, requiring only an elementary understanding of BI tools.

However, Data Studio has its limitations regarding live updates. To help you out, we’ve created a comprehensive guide to collecting and crafting live dashboards in GDS using Python functions:

A Quick Glance at Google Data Studio

Before we delve into its intricacies, let’s take a quick Google Data Studio overview. GDS is an innovative data visualization tool that equips users with the features to generate customized and interactive dashboards.

With Google Data Studio’s functions, companies and individuals can track crucial KPIs, collaborate on private dashboards, and visualize business trends. Furthermore, users can create in-depth reports to analyze performance and make data-driven business decisions.

What Does Data Studio Connector Mean in GDS?

When creating stunning Google Data Studio reports, users need to leverage connectors. Since Google provides connectors within GDS, offering a live connection to multiple data sources.

For instance, if you want to load a live dashboard or a real-time report, you must first select the ideal connector, such as Google Analytics. Since Data Studio loads data slowly, most people prefer using data extracts, resulting in up-to-date info.

What Are the Benefits of Data Studio?

Google Data Studio is a robust tool for businesses, offering the following primary benefits:

  • Super easy to set up and affordable
  • Produces customized and interactive reports
  • Facilitates campaign monitoring and collaboration
  • Minimizes your turnaround time on data visualization and reporting
  • Offers numerous Google Data Studio integrations to promote sharing

Why Do You Need Real-Time Data Visualization on Google Data Studio?

While GDS offers robust features to create live dashboards, the tool does not provide live updates. Thus, users must refresh their Google Data Studio SEO reports to refresh real-time data and access up-to-date information.

Unfortunately, most people lack the time and patience to do manual work to update their GDS reports every few minutes. The reason that Data Studio shows old info is that it gathers datafrom numerous connectors to populate its dashboard widgets.

As a result, the software keeps the data temporarily cached, updating only when the user hits the refresh button, whether the browser or the manual one. When it comes to static data, you’ll have to use the refresh button within GDS, or your report widgets will remain the same.

Sounds tiring, right? Fortunately, there’s a better solution involving Python programming to gather and update real-time reports!

How to Build Real-time Dashboards with Python on Google Data Studio

If the manual Google Data Studio report refreshing sounds like a snooze, Python and Google Sheets are here to save the day! Here is a step-by-step guide to building real-time visualization dashboards on GDS:

Step 1: Create Your BigQuery Project

The best way to eliminate the need to use the manual refresh button is to use a platform that supports Python documentation. So, start by creating a new BigQuery project, as the tool integrates with GDS.

If you’ve never used BigQuery before, the platform offers a fantastic free tier with sufficient queries. The next step is to open a source (CSV) file compressed by ZIP. While the Google Sheet has multiple rows and columns, you’ll only need to use several crucial ones, such as DUID and settlement date.

Step 2: Write a Complex Python Script

Create a new tablet using a partition per day, clustered by the DUID field. Once done, you can start writing your Python script based on your unique needs to load data to BigQuery.

We recommend scheduling your script to run every three to five minutes to promote real-time data fetching and visualization.

Step 3: Add the Features You Want

While writing your Python script, you can add dimension tables to showcase your desired features. After that, create a holistic view that joins the two tables and filters the rows to eliminate duplicates. You can utilize the Python list remove function to remove errors and doubles on your script efficiently. Next, click on the “Share” menu and navigate the “Embed Report” button to embed the document into a usable HTML webpage.

Step 4: Make Your Google Data Studio Report

Now, it’s time to create your Data Studio report! For this, you’ll have to create two connections, a live and import connection. Start by pulling “today’s” data and deactivate the “Enable Cache” button.

After that, you won’t have to worry about clicking the refresh button manually.

Step 5: Import and Enjoy!

Use the import connection, called extract, to load the data you created on Python on Google Data Studio. For this, select the “Upload File” button in GDS and ensure you check its size, as you can import a maximum of 100 MB.    

Once done, you can sit back and watch GDS visualize real-time dashboards and reports.

The Bottom Line

Google Data Studio is a powerful tool for marketers, data analysts, and data scientists seeking data visualization and reporting solutions. With this software, you can enjoy benefits such as ease of setup, customization, and collaboration.

However, its limitations regarding real-time updates can create frustrations and damper your business’s efficiency when monitoring dynamic data. Thus, to overcome this and make timely decisions, Python can be a valuable ally, continually updating live dashboards on GDS.

With this automation, you can save time and ensure you have access to the latest information without manual clicking, resulting in stunning reports and an interactive dashboard. As a result, you can create a robust and efficient solution for professionals that match the best Google Data Studio dashboard examples.

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