Web scraping can help you extract different types of data. You can scrape real estate listings, hotel data or even product data with pricing from eCommerce websites by writing a few lines of code. But if you are going to crawl the web and extract data, you need to take care of a few things. It is important to make sure that the scraped data is in a usable format. Your code can extract data from multiple websites. The extracted data is clean and does not give erroneous results when running algorithms. That said, if you want to write your code or create a small web scraping project to scrape data from websites, you can do so in five simple steps.
“A man is as good as his tools”. Hence you need to choose your tools for web scraping in a way that suits your needs. While some ready-to-use software may seem easy to use, they might not allow you to make a lot of configuration changes. At the same time, many tools may not have a plugin to save the data that you scraped in a database or on the cloud. When it comes to programming languages that are used in web scraping today, there’s Node.js, Python, Ruby and more. But among these, Python is the most popular one thanks to its easier learning curve, simple syntax, the availability of multiple external libraries and the language being open source.
There are multiple libraries like BeautifulSoup and Scrapy that can be used to scrape web pages, create web crawling spiders and run scraping jobs at specific time intervals. Python allows for immense flexibility when it comes to integrating other systems with your scraping engine. You could easily save your scraped data on your local machine, in databases, on S3 Storage, or even dump them into a Word or Excel file.
Before you go about scraping product information from say a thousand product pages on Amazon, you need to access the page and get the entire Html page to analyze it and decide on a strategy. Data in Html pages can reside within specific key-value pairs in tags or the text within tags. You can use libraries like BeautifulSoup to specify which exact tag you want to extract data from on every webpage, and then run the code on a loop. This way, for every single product web page, your code will run and extract the same information- say, price details.
Even before you start scraping the data, you should decide on where you will be storing the data. This is because how you process the data will depend on where you will be storing it. There are multiple options available. You can choose between NoSQL and SQL databases, depending on whether the data that you are scraping will be structured on unstructured. For unstructured data, you can choose SQL databases since you can store the data in rows consisting of a set of attributes. For unstructured data, where there are no set attributes, you can go for NoSQL databases. In terms of what database to save the data in, for SQL you can choose MySQL or Postgres. Amazon RDS offers on-the-cloud databases where you can store your data and pay based on usage.
For NoSQL, you can choose one of their fully managed and extremely fast solutions like DynamoDb or ElasticSearch. Different databases have advantages that offer fast retrieval, some offer cheaper storage per TB. The database you choose depends on your specific use case and hence. Some research needed on this before you decide on one. In case you need to store scraped images and videos that are large, you can use AWS S3 or Glacier. The latter used when you want to store massive amounts of data in an archived format. You would not need to access often while the former is the more used solution. This acts as something like an online hard drive. You can create folders and save files in them.
While you may test your code on a single webpage, you must be wanting to scrape tens or hundreds of pages because of which you are undertaking this project. Usually, if you are going to scrape a few web pages, you can save the links in an array and loop over it when scraping the pages. A better and more frequently used solution is to use a regex. Simply put, it is a programmatic way to identify websites with similar URL structures.
For example, you might want to scrape product data of all the laptops on Amazon. Now you might see that all the URLs begin with “www.amazon.com/laptop/<laptopModelNo>/prodData”. You can replicate this format using regex so that all such URLs extracted and your web scraping function running on only these URLs. And not all the URLs on Amazon’s website. In case you have too many web pages to crawl, we recommended that you use a parallel processing approach to scrape around ten web pages at any moment. If you are scraping web pages and extracting links from them, and then scraping the web pages that those links lead to, then you can use a tree-like approach to scrape multiple child pages arising from a root webpage at the same time.
Everything we have discussed until now has been the preparation. For the final act of running the code and getting the job done. Web scraping code rarely works exactly the way you are expecting it to. This is because not all web pages that you are trying to scrape would have the same structure. For example, you run your scraping code on 100 product pages and find only 80 of them have been scrapped. The reason behind this is that 20 pages may be in the out-of-stock state and their webpage structure is different. Such exceptions will not account for when you write the code. But after a few iterations, you can make the required changes. And extract data from all the webpages that you need.
Scraping up to a few hundred pages(while making sure you give a few seconds gap in between each run). Scraping a website once a month would work fine with a DIY solution written in Python. But in case you are looking for an enterprise-grade DaaS solution, our team at PromptCloud provides an end-to-end solution where you give us the requirement and we hand you the data which you can then plug and play.
Infrastructure, proxy management, making sure you don’t get blocked while scraping data. Running the scraping engine at a regular frequency to update the data. We also make changes to accommodate changes made in the UI of the concerned website. Everything is handled in our fully managed solution. This is a pay-per-use cloud-based service. This will fulfill all your web scraping requirements. Whether you are handling a startup, an MNC or you need data for your research work. We have data scraping solutions for all.