Building a Scraping Bot to Fetch Data and Improve Search Capabilities

Phone Numbers Scraping Bot

My client had a need to display data from one website on another, but faced a limitation that prevented the use of an API to fetch the data. To solve this challenge, I built a scraping bot using Python and Selenium hosted on AWS Lambda, triggered by an API call. This allowed me to scrape the data and return it as an API response to the requesting website.

Objectives

  • Display data from one website on another
  • Bypass limitations preventing the use of an API
  • Create a scraping bot using Python and Selenium
  • Host the bot on AWS Lambda and trigger it through an API call
  • Save data on the website’s database to reduce the frequency of running the Lambda and thereby reduce costs
  • Improve loading times by fetching data from the website’s database

Process

My client had a need to display data from one website on another, but faced a limitation that prevented the use of an API to fetch the data. Specifically, the website that our client needed to scrape did not provide an API, making it impossible to obtain the data through traditional means.

To solve this challenge, I built a scraping bot using Python and Selenium hosted on AWS Lambda, triggered by an API call. Python was chosen for its simplicity and flexibility, allowing me to quickly prototype and build the scraping bot. Selenium was chosen for its ability to automate browser actions, which was essential for navigating and scraping data from the target website.

I first researched and planned the solution, selecting the appropriate tools and hosting platform. I then built the scraping bot using Python and Selenium, with the ability to search for data on the target website. I hosted the scraping bot on AWS Lambda and triggered it using an API call.

Results

After implementing the scraping bot on the client’s website, I was able to successfully fetch data from the target website and return it as an API response. By utilizing Python and Selenium, I was able to overcome the limitation of the target website not having an API and automate browser actions to obtain the desired data.

Additionally, I implemented a solution to save the scraped data on the client’s website database. This allowed me to reduce the frequency of running the Lambda function, which in turn helped to lower costs. By fetching data from the website’s database instead of running the Lambda function each time the data was needed, I was able to improve the loading times for the end-users, enhancing their experience.

Risks

Scraping data from other websites comes with inherent risks that must be considered before undertaking such a task. One of the most significant risks is scraping data from websites that prohibit scraping. In such cases, scraping data without obtaining the proper authorization can lead to legal consequences.

Furthermore, scraping data from websites that do not provide an API may violate their terms of service. To mitigate these risks, it is important to ensure that scraping is allowed by the target website or obtain proper authorization before proceeding with the scraping process.

Another risk to consider is the possibility of being blocked by the target website if scraping too much data too quickly. This can lead to a denial of service, which can cause significant disruptions to the target website. To mitigate this risk, it is important to limit the scraping rate and to monitor the amount of data being scraped. Implementing measures such as random delays and user agent rotation can also help avoid being blocked by the target website.

In this case study, I took steps to mitigate these risks by researching and planning the solution, selecting appropriate tools and hosting platforms, and implementing QA tests to ensure that the scraping bot worked as expected without causing any disruptions. By being aware of the risks involved in scraping data from other websites, I was able to implement an effective solution that provided the client with the desired data while avoiding any potential legal or technical issues.

Conclusion

By building a scraping bot using Python and Selenium hosted on AWS Lambda, I was  able to bypass limitations preventing the use of an API and fetch data from a target website. By saving data on the website’s database and fetching it from there, I was able to optimize costs and improve loading times. My solution enhanced the client’s website search capabilities and improved the user experience.

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