Preparing for Effective Web Data Scraping
Web scraping is becoming the go-to technique for collecting data from public websites. When the data that you are looking for is not available from sources like data.gov or communities like Kaggle, you now have the option to collect your own data from various sources.
For example, you can easily find data of top restaurants and the reviews they have received from customers by scraping websites like Yelp. You can also use web scraping to find the email addresses of decision-makers in companies via sources like social media.
Web scraping is becoming more accessible, but that doesn’t mean you can just jump right in and start collecting data from the web. There are a few preparations needed for an effective data scraping operation.
Covering the Basics
The first thing you want to set up is the tools. Web scraping has been deemed legal because you are accessing and collecting data from public sources, but websites still have terms and conditions that prevent large-scale data scraping from affecting their website traffic.
To get around this limitation, you need a reliable proxy service. Getting a US proxy service is easy now that you have providers like Smartproxy making their services available to more users. You can learn more about proxies from any state and major city in the US and find a suitable one right away.
A US proxy also guarantees access to the most suitable data. A fresh IP address means you will not visit web pages that have their content tailored to your browsing history. At the same time, you are also ensuring the smooth operations of your web data scraping.
Next, you need a good web scraping tool. The options are endless; web scraping has been very popular across multiple industries. Tools like ParseHub can help you turn data on websites into a spreadsheet for further processing.
Similar tools also work with JSON output through an API, giving you the option to automate data processing with additional tools. Data processing tools like Google’s Big Query can be used to process data that you already store in a database.
If you want to go down the advanced route, you also have the option to use tools built on a programming language like Python and Go. There are pre-made frameworks that allow you to be very specific in defining the parameters of your web scraping operations.
Collecting Relevant Information
The next part of the preparation is identifying the data that you want to collect. This is a process that starts with setting clear objectives for the scraping operation. You need to know why you are scraping the World Wide Web for information before you can define other parameters such as sources.
There are many reasons why web scraping is performed. You can, for instance, use web scraping to find potential leads based on specific keywords. When marketing solutions to companies in a particular industry, web scraping can help find leads to automatically add to your CRM.
Another popular use of web scraping is for price intelligence. Having competitive prices is a great way to stay ahead of the market, especially when you are in industries like FMCG or retail. Through web scraping, you can eliminate the manual work of getting and comparing prices entirely.
Web scraping is also handy for creating data warehouses that support things like trend analysis and market analysis. Even financial firms are now using web scraping to better monitor market sentiment and to generate alerts when big market changes happen.
With the objective defined, you can then identify the sources for your data. Keep in mind that you have to configure specific sources for web scraping to be effective. You can use crawlers – another handy tool to have – for the job.
Getting data from incorrect sources will result in your database or data warehouse being filled with irrelevant information. In other words, you may end up with more noise than valuable data, which will make analyzing the output of web scraping more difficult.
Once the sources are identified, you can then identify the information that you want to collect. The best way to do this is by sampling a few pages from the source, checking how data is displayed on those pages, and then defining the scraping parameters to match what you see.
Getting It Running
As mentioned before, most web scraping tools can work from your machine or in the cloud. For shorter web scraping operations, running the scraping tool on your computer is a good idea. Since you already have a US proxy in place, you don’t have to worry about getting blocked by your sources.
On the other hand, longer scraping operations – and automated ones, which usually run periodically over a predetermined range of time – are best run from the cloud. Cloud servers are persistent and highly available. You don’t have to keep your own computer running to acquire the necessary data.
Web scraping can be a lot more powerful when you add automation to the mix. When outputting to a spreadsheet, for instance, you can define the output file as a Google Sheet file, and then have tools like Zapier or SalesForce perform automated tasks when new rows are added to the sheet.
The same is true with data processing. Tools like Big Query can help optimize your data using filters and other database operations. For example, you can automatically separate relevant data from noise using simple queries that check data formats or the existence of certain data points.
The combination is a powerful one. Using price intelligence as an example, you can automate price adjustments on your e-commerce website using prices from other retailers as the triggers. Even platforms like WooCommerce support API calls that help automate this type of process.
The last question to answer is whether web scraping is a sustainable way of collecting information, and the answer to this question is a definite YES. With the help of proxy servers and all the precautions and preparations that we have discussed in this article, you will be able to leverage public data from multiple sources to gain a competitive advantage on the market.