How to Pull Google Reviews for Places Using Google API into Power BI: A Step-by-Step Guide

-

 

 As a data enthusiast, one of the most common asks I encounter is: How do we pull reviews from Google for specific places and load them into Power BI for analysis? The process may seem daunting at first, but with a clear roadmap, you'll find it's not only manageable but also incredibly insightful. In this guide, I’ll Walk you through every single step—right from setting up the API to visualizing the data in Power BI.

 

 Step 1: Understanding the Google Places API

Google’s Places API allows us to retrieve data such as details about a place, its reviews, ratings, and more. To access this data, you'll need a Place ID (unique to each location) and an API key from Google Cloud.


 Step 2: Set Up a Google Cloud Project

Before making any API requests, you need to set up your Google Cloud environment:

 

    1. Log in to Google Cloud Console 

   Visit [Google Cloud Console] (https://console.cloud.google.com/) and log in with your Google account.

 

      2. Create a New Project 

   - Click on the dropdown in the top left corner and select "New Project."

   - Give it a name, e.g., "Google Reviews for Power BI."

 

      3. Enable the Places API 

   - Go to APIs & Services > Library. 

   - Search for Places API and enable it for your project.

 

     4. Generate an API Key 

   - Navigate to APIs & Services > Credentials. 

   - Click "Create Credentials" and select API Key. 

   - Copy and save this key securely.

 

 Step 3: Get the Place ID for Your Desired Location

To retrieve reviews, you need the Place ID of the location. Here's how:

 

   1. Go to the [Google Maps Place ID Finder]               (https://developers.google.com/maps/documentation/javascript/examples/places-placeid-finder).

   2. Search for your location (e.g., "Greens Storage, Escondido").

   3. The Place ID will appear below the location name. Copy this ID. For example: 

   `ChIJN1t_tDeuEmsRUsoyG83frY4`

 

 Step 4: Construct the API Request URL

With the API key and Place ID, you can construct the URL to fetch place details, including reviews. 

The format for the URL is: 

```

https://maps.googleapis.com/maps/api/place/details/json?placeid=<PLACE_ID>&key=<API_KEY>

```

 For example: 

```

https://maps.googleapis.com/maps/api/place/details/json?placeid=ChIJN1t_tDeuEmsRUsoyG83frY4&key=YOUR_API_KEY

```

 Step 5: Test the API in a Browser or Postman

Before loading data into Power BI, test the API in your browser or a tool like Postman:

 

1. Paste the URL in your browser.

2. The JSON response will display place details, including reviews. If not, ensure you’ve enabled the Places API and entered the correct Place ID.

 

 Step 6: Load the Data into Power BI 

    1. Open Power BI Desktop 

     Start Power BI and select "Get Data" > "Web".

 

    2. Enter the API URL 

    - Paste the API URL (constructed in Step 4). 

    - Click OK to fetch the data.

 

     3. Authenticate (If Needed) 

   - Power BI may prompt for authentication. Select Anonymous if your API key doesn’t require           additional restrictions.

 

     4. Preview the JSON Data 

     - Power BI will display the JSON structure fetched from the API.


 Step 7: Transform JSON into a Table

    Once the JSON data is loaded, you need to extract the reviews:

      1. Expand the JSON 

       - In the Power Query Editor, locate the `result.reviews` field. 

        - Click the expand icon (a small box with arrows) next to it.

 

       2. Select Review Fields 

       - Choose fields like `author_name`, `rating`, `text`, `relative_time_description`, etc., from the list. 

        - Click OK to transform the JSON into tabular format.

 

       3. Rename Columns 

       - Rename the columns for clarity, e.g., "Reviewer Name," "Review Text," "Rating," etc.

 

       4. Load Data 

       - Click Close & Apply to load the transformed data into Power BI.

 

 Step 8: Visualize Reviews in Power BI

With the data loaded, it’s time to create visuals:

 

     1. Use a Table or Matrix 

   - Add a table visual to display reviews with fields like "Reviewer Name," "Rating," and "Review          Text."

 

     2. Add Summary Visuals 

     - Use a card visual to show the average rating.

    - Create a bar chart to display the distribution of ratings (e.g., 1-star to 5-star).

 

     3. Filter by Location 

    - If you’ve pulled reviews for multiple locations, use slicers to filter by Place Name.

 

  Automate Data Refresh

To keep your reviews up to date:

 

1. Set up a Scheduled Refresh in Power BI Service. 

2. Ensure your API key doesn’t expire or set restrictions only for your application or IP.


 Key Points to Note

- Quota Limits: 

  Google provides a free tier with limited API requests per day. For extensive usage, you may need to upgrade your Google Cloud account.

 

- Data Accuracy: 

  Reviews are user-generated, so ensure they align with your analysis goals.

 

- Privacy Considerations: 

  Be mindful of how you use and share review data.


 Conclusion 

Integrating Google reviews into Power BI opens up a world of possibilities for analyzing customer feedback. By following these steps, you can not only fetch reviews but also gain actionable insights from them. Whether you're tracking customer satisfaction or comparing locations, this workflow is both powerful and flexible.

Got questions or need help? Drop a comment or connect with me—I’d love to hear your thoughts and help with your data journey!

Comments

Post a Comment

Popular posts from this blog

A Complete Guide to SnowSQL in Snowflake: Usage, Features, and Best Practices

-
  As cloud data platforms continue to grow in complexity, users need more effective tools to interact with their data environments. Snowflake, one of the leading cloud data platforms, provides SnowSQL , a powerful command-line client designed for executing SQL queries and interacting with the Snowflake ecosystem. Whether you're a data engineer, a data analyst, or just a Snowflake enthusiast, understanding how to use SnowSQL is crucial to fully leveraging Snowflake's capabilities. In this blog post, we’ll explore SnowSQL in depth—covering everything from installation and basic commands to advanced features, configuration, and best practices. By the end, you'll be well-equipped to use SnowSQL in your own Snowflake workflows, maximizing efficiency and productivity in your data operations. What is SnowSQL? SnowSQL is the command-line client for Snowflake, enabling users to interact with Snowflake’s data warehouse and perform SQL queries, administrative tasks, and data man...
Read more

Mastering DBT (Data Build Tool): A Comprehensive Guide

-
  In today's fast-paced data-driven world, organizations need a streamlined and scalable way to manage their data transformation processes. Enter DBT (Data Build Tool) – an open-source tool that has quickly become the gold standard for data transformation, providing data engineers, analysts, and teams with an efficient, maintainable, and scalable way to manage analytics workflows. DBT has garnered widespread adoption due to its ability to handle complex data transformations, automate workflows, and allow users to focus on analyzing data rather than managing the infrastructure. In this comprehensive guide, we'll dive deep into DBT, its core features, how to use it, and why it's a game-changer for modern data teams. What is DBT? DBT (Data Build Tool) is an open-source command-line tool that allows data analysts and engineers to build, test, and document data transformation workflows in SQL. It is designed to run on top of cloud data warehouses like Snowflake , BigQuery ...
Read more

Unleashing the Power of Snowpark in Snowflake: A Comprehensive Guide

-
  Unleashing the Power of Snowpark in Snowflake: A Comprehensive Guide In the world of modern data engineering and analytics, Snowflake has emerged as a leader in cloud-based data warehousing. Known for its scalability, ease of use, and robust architecture, Snowflake has transformed the way organizations manage and analyze their data. A key feature that takes Snowflake’s capabilities even further is Snowpark . Snowpark enables developers, data engineers, and data scientists to write and execute complex data processing pipelines directly within the Snowflake environment. It allows for a seamless integration of advanced data manipulation capabilities with the scalability and performance of Snowflake’s platform. In this blog post, we’ll dive deep into Snowpark, how it works, and how you can leverage it to streamline your data workflows. What is Snowpark? Snowpark is a developer framework that allows you to write, execute, and manage data transformations inside Snowflake using pop...
Read more