Streamlining AI Data Collection with Bright Data’s Scraping Browser

Bright Data infrastructure is championed around collecting only publicly available data, backed by an industry-leading know-your-customer (KYC) process and a transparent, acceptable use policy. Rated number one by customers on G2, Bright Data provides two scraping solutions, the and the , that simplify the data collection process.

This guide covers the following:

• Advantages of using Bright Data’s scraping tools over traditional methods
• Practical demonstration of building a dataset with a scraping browser
• Overview of Bright Data’s dataset marketplace for ease of use

Let’s get started.

Advantages of using Bright Data’s scraping tools

The Scraping Browser is one of the industry’s most cutting-edge scraping tools. With this tool, you can run your scripts on fully hosted browsers equipped with a CAPTCHA auto-solver, unlimited scalability, and residential IPs to enhance data collection.

According to Bright Data CEO Or Lenchner, the Internet is the world’s largest database; the only issue is organizing its data. Using the Scraping Browser, Bright Data helps you manage your data with its in-built unblocking and hosting properties.

Some of the advantages of using Bright Data’s scraping tool includes:

Reason #1: Efficiency

Bright Data comes embedded with a developer-first dynamic scraping ability with pre-written scripts with different scraping technologies, making it possible to accelerate the data collection process compared to traditional methods.

Reason #2: Reliability

Data companies trust Bright Data tools for their robustness and stability in handling large-scale data collection across their vast residential IP pool and network, ensuring high-quality and accurate data. Another critical point to note is that Bright Data offers support and resolves issues quickly for its customers around the clock.

Reason #3: Global adoption

Bright Data powers many global brands, which is interesting to note with Bright Insights. Bright Insights leverages deep technology infrastructure to transform public data into actionable insights that serve more than 20,000+ businesses with crucial public web data. Some notable companies that use Bright Data are Microsoft, Epson, Mozilla, and so on.

One widespread use case where companies use Bright Data for their data collection needs is using data for AI. In such a scenario, Bright Data assures its customers and users (data scientists) that they always have AI training data for their machine learning models, providing everything you need from discovering, curating, and collecting web data at scale.

The four essential components of the data from Bright Data are:

• Continuously refreshed
• Clean and validated
• Compliant and ethical
• Scalable and performant

  
  

Check out the different data types used in AI models to learn more about using datasets to train AI models and LLMs.

Practical demonstration of building a dataset with a Scraping Browser

According to Bright Data documentation, the Scraping Browser is one of their proxy-unlocking solutions designed to help you focus on your multi-step data collection from browsers while taking care of your full proxy and unblocking infrastructure.

Some of the benefits of the Scraping Browser are:

• Boost developer productivity
• Cut infrastructure overheads
• Increase success rates
• Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium

To get started, follow the instructions in the that describes how to use the Scraping Browser. and receive a $5 credit.

For a more profound practice on integrating and using Playwright with Python, this guide takes you through scraping public web data.

Overview of Bright Data’s dataset marketplace

Get fresh datasets from the , a repository of datasets from popular public websites. Browse the dataset category and select the one you want to use.

The dataset pricing model is calculated based on the number of records used, whether one-time, biannual, quarterly, or monthly.

Advantages of using the marketplace

1. No-code web scraping
2. Strict validation methods
3. API for on-demand data

Conclusion

In this article, we discussed the importance of the Scraping Browser and the readily available pre-built datasets in the marketplace. Before scraping or understanding data from a company dataset, you need not be technical.

Streamlining data serves as a means to support companies needing extensive training data and capitalizing on building efficient models.

Finally, , the future of web scraping is intricately tied to ML and AI technologies. In 2024, scraping tools will become more intelligent, and the need for manual intervention will diminish. The implications of this can raise concerns about data privacy and ethical use if it scrapes sensitive information from the target site without consent.

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