Web scraping allows you to pull data from websites, which is used to examine and gain information that helps to improve decision-making. Power BI is a business intelligence tool used by many organizations to fetch information and create interactive dashboards. Power BI is not only used as a visualization tool but also as a web scraping tool. In this blog, you will understand the scraping of data with Power BI with visualizations in detail.
Table of Contents:
What is Web Scraping in Power BI?
Web scraping is a process of extracting information from websites using tools and scripts, and also includes downloading the HTML File of a webpage. The process of web scraping includes parsing HTML content to collect information. It usually includes downloading HTML files of web pages. This process is very important for data analytics as it helps to fetch hidden information.
Why Use Power BI for Web Scraping?
Power BI is considered a data visualization tool, but also a web scraping tool that allows you to connect to various data sources and extract information from them. Power BI allows an easy connection to websites and extracts data. It transforms your data and makes sure that your data is clean. Power BI uses a Web connector, which allows you to connect to websites.
Power BI vs Python for Web Scraping – When to use what?
| Python for Web Scraping |
Power BI for Web Scraping |
When to Use What |
| Requires coding knowledge (e.g., Python, HTML, CSS) |
No coding needed; uses
Power Query web scraping or Power BI web data connector |
Use Power BI when ease of use is a priority |
| Flexible: handles dynamic content, login pages, JavaScript |
Limited to static HTML and API responses |
Use Python for complex or dynamic websites |
| Uses libraries like requests, BeautifulSoup, Scrapy, Selenium |
Uses Power Query web scraping, Power BI Web Data Connector, and built-in UI |
Use Power BI for quick data grabs and basic transformation |
| Suitable for automation and large-scale scraping |
Best for lightweight or one-off web scraping tasks |
Use Python for scalable or frequent scraping needs |
| Requires external tools to schedule (e.g., cron, Task Scheduler) |
Can refresh data automatically in Power BI Service (Pro or Premium) |
Use Power BI when dashboard integration is needed |
| Separate tools needed for visualization (e.g., matplotlib, Plotly) |
Can visualize web data in Power BI instantly |
Use Power BI for seamless reporting and dashboards |
| Fully customizable error handling and parsing logic |
Limited customization and error handling |
Use Python when you need fine control over logic |
| Stores data in files, databases, or APIs |
Data stays within Power BI’s model or service |
Use Power BI when you don’t need external storage |
| Handles CAPTCHAs, authentication, and cookies |
Cannot handle complex auth flows |
Use Python for login-protected or interactive sites |
| Ideal for building robust ETL pipelines |
Ideal for integrating clean, structured web data into reports |
Use Power BI when working with clean, structured web data |
Advantages of Using Power BI for Web Scraping
- Easy Integration: If you are using Power BI for data analysis, then using web scraping can help you extract data from website directly without needing another tool.
- Data cleaning: With web scraping, Power BI is also used to clean the scraped data, which makes it ready for operation.
- User-Friendly Interface: Power BI is easy to use and has an interface similar to MS Excel. You don’t need to know any professional-level programming to work on Power BI.
- Great for Visualizing Data: After web scraping, you can use visualization charts to make your data easier to understand.
Step-by-Step Guide to Scrape Web Data in Power BI
Let us look into the process of how to scrape data from a website and load it into Power BI
For example, let’s consider that we want to scrape Intellipaat’s Data Science data that contains students of the Data Science batch of Intellipaat Software solutions.
Step 1: Launch Power BI Desktop to Start Web Scraping from a Website
This is how the first page, called report view, opens when you click on Power BI. This section is called report view, as you can create different reports and dashboards with the help of the different graphs available in the visualization panel
- Click the Get Data button on the Home ribbon and select Web from the available data sources
These are common data sources that are available in Power BI, and we will be choosing web, which is used to extract information from the web.
You will need to input the URLs of the websites from which you want to scrape data. Power BI will attempt to load and show information.
Step 3: Navigate and Preview Web Data in Power BI for Extraction
Power BI will analyze the page and try to extract information found in the HTML structure. If the website is well-structured, you will see several tables, so select the table that contains the data you need.
Click on Transform Data after you have selected your table, and you can use the Power Query web scraping Editor to clean and transform your data.
This is what cleaned data looks like.
Click Close and Load to load the data into Power BI once the data is cleaned and transformed.
Step 6: Visualize Web Data in Power BI Using Interactive Dashboards
Data is now in Power BI; you can start building dashboards and reports. You can create graphs like pie charts, bar charts, tables, donut charts, and many more, which will help you to analyze your data very well.
This chart explains the Count of candidates by Country
From the country India, Intellipaat had 66.67% of students who took the Data Science course
Respectively, from South Africa it was approximately 6.67%, and in the USA it was 26.67%
The funnel chart shows the Number of students who took the Data Science Course from Intellipaat and their designation.
Precautions and Limitations of Web Scraping in Power BI
- Check the Website’s Rules: Many websites do not allow for web scraping, so check the terms and conditions of the website that you are going to scrape
- Do not overload the website: Scraping frequently can slow down the website and your system’s performance. To prevent these issues, Power BI allows you to schedule how frequently it pulls data, which helps you to avoid overloading the website
- Be Prepared for Security Measures: Some websites use security features like CAPTCHAs or IP blocking to prevent irrelevant scraping. Power BI can’t always get through these protections, so you may need extra tools or code like Python to pass these issues.
- Watch out for Dynamic Content: Some content on websites is dynamic and built on JavaScript, and Power BI might not be able to handle it alone. For these, you may need additional tools like a Python script to extract data.
Use Cases and Real-Life Applications
1. Market Price Monitoring
- Use Power BI for web scraping to pull product prices from online stores into a dashboard for quick price comparison.
- Python can be used to scrape the same data, store it in a database, and perform trend analysis over time.
2. Competitor Analysis
- Extract data from website Power BI to monitor competitor blogs, product releases, or pricing updates.
- With Python, businesses can automate and scale competitive intelligence gathering across many sources.
3. News and Sentiment Analysis
- Visualize web data in Power BI by scraping headlines or article summaries from news websites.
- Python can include natural language processing (NLP) to classify news sentiment before sending it to Power BI for visualization.
4. Public Data Collection
- Government or institutional sites often offer structured HTML tables, perfect for Power BI web data connector usage.
- For unstructured or PDF-based data, Python is a better choice.
5. Social Media Monitoring
- Python is more commonly used here with authentication and scraping capabilities from public-facing social feeds.
- Power BI can be connected to social APIs, but scraping social content manually is often restricted or complex.
Best Practices For Web Scraping in Power BI
- Check for Websites: Some websites have terms of service that prohibit scraping.
- Don’t scrape data too regularly, as it can put unnecessary stress on website servers. Power BI lets you set a data refresh interval to make sure you are scraping correctly.
- Some websites use JavaScript to load interactive content, so to load dynamic content in Power BI, some additional tools are required, like Python scripts or an API to scrape dynamic content.
- Data quality can be changed while scraping, so try to focus on your data and refresh the processes to make sure everything works correctly.
Conclusion
Power BI is a powerful tool for web scraping, which allows you to fetch data from a website, clean it, and turn it into visualizations. Power BI, with its simple interface, makes scraping data easy. However, it is important to be aware of legal policy and the concerns of web scraping. Always check the website’s terms and conditions before scraping. By following best practices and being careful with how often and how much data you pull, you can make Power BI a powerful tool that helps businesses make better decisions.
To learn more about Power BI and its functions, check out this Power BI Course and also explore Power BI Interview Questions prepared by industry experts.
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Web Scraping with Power BI- FAQs
Q1. Can we do web scraping in Power BI?
Yes, Power BI allows you to scrape data from websites.
Q2. Which language is commonly used for web scraping in Power BI?
Python is the most common language used for web scraping.
Q3. Is web scraping illegal?
Web scraping is not illegal, but it depends on the company policy and their Terms and Conditions.
Q4. Which ETL tool is used in Power BI?
Power BI uses Power Query web scraping Editor, which helps users to extract, transform, and load data.
Q5. How do I get web data from Power BI?
Open Power BI > Get Data>web.
Q6. How to use Power BI for web scraping?
Use Power BI’s “Get Data from Web” feature in Power Query to extract and transform structured or semi-structured data directly from web pages.