Its use is very clear: we can take advantage of web scraping to get industrial quantities of information (Big data) without typing a single word. Through the search algorithms, we can track hundreds of websites to extract only the information we need.

To do this, it will be very useful for us to master regex (regular expression) to delimit searches or make them more precise and to filter information better.

Some examples of which we are going to need the web scraping:

For content marketing: we can design a robot that makes a ‘scrape’ of specific data from a website and we can use it to generate our own content. Example: scrape the statistical data from the official website of a football league to generate our own database.

To gain visibility in social networks: we can use the data of a scrape to interact through a robot with users in social networks. Example: create a bot on Instagram that selects the links to each photo and then programs a comment in each entry.

To control the image and visibility of our brand on the internet: through a scrape we can automate the position by which several articles of our website are positioned on Google or, for example, control the presence of our brand name in certain forums. Example: Track the position in Google of all the entries in our blog.

How does a web scraping work?

Let’s give a basic example of how a web scraper works. Imagine that we are interested in extracting the title of 400 pages that have the same format and are located on the same site. In each of the 400 pages the title is inside a selector <h1>that in turn is inside a <div>with the class .header.

What our web scraper will do is detect that selector h1that is inside the header class (.header h1) and extract that information in each of these 400 pages. Then we can obtain all that information through the export of the data in formats such as a list in .jsonor a file .csv.

What we would manually take a few hours of absolute boredom and mechanical work our web scraper can do in just a couple of minutes.

What knowledge do you have to have to be a good web scraper?

Web scraping is a discipline that must combine two very different aspects of web knowledge, both essential to have a versatile profile on the web. We must dominate the visualization of data at the conceptual level and, on the other hand, we must have the necessary technical knowledge to extract accurately the data with specialized tools.

At the end of the day, this will be summarized in knowing how to manage large amounts of data (big data). We should be minimally familiar with the visualization of large amounts of data in order to hierarchize and interpret the data we extract from a website. And not only to extract the data, also at the time of planning the extraction strategy we must know what the data we are going to extract will be in order to give them an informative sense for the user.

There are 3 key points that we must master to be good web scrapers:

  1. Knowledge of web layout. Web scraper works by selecting HTML selectors and for this, we will need to have four basic knowledge of web architecture.
  2. Know how to use software to visualize data such as a Google spreadsheet processor, known as Google Spreadsheets, or a basic text editor such as Sublime.
  3. Have knowledge of regex. Having minimal knowledge of regex (also called regular expression) will make it much easier for us to work with large amounts of data since it can save us thousands of hours of hard work when correcting or debugging data before importing them. to the desired platform.

And after the web scraping? How to use the obtained data

The web scraping is to obtain the data but obviously, this data will have to be used for some purpose. This is where two key processes come into play once the data is obtained:

Nesting, ordering, and filtering of data. Many times when we extract industrial quantities of data, before importing them to another platform we will have to ‘work’ these data with precision with such debugging them for import.

Import of the data to another platform. The import of the data is about another basic process. There are highly recommended tools with which we can work on platforms such as WordPress, such as the WP Ultimate CSV Importer plugin from the Smack Coders development studio (they also have a paid version, Ultimate CSV Importer Pro ).

What tools are there to do web scraping?

Without a doubt, I would opt for two main tools: diggernaut.com and import.io. Another third tool would be Scrapy.org. Below is a brief description of each of the tools together with a small assessment of who they will be useful for:

Diggernaut.com

It is a cloud platform for web scraping and it offers both: visual editor to build web scrapers without any knowledge in programming or HTML and web generally, and scraping metalanguage (SML) which can be used as the substitution for any programming language as its easy and plain. SML requires some expertise in HTML, CSS, general knowledge about the web and regular expressions (if you are planning to extract parts of the text). Another important feature that you can run your scrapers in the cloud, or you can compile it and run on your computer or server.

Import.io

It can be used from the control panel of the web for basic scraps, although for more complex operations it is necessary to download the program. The program is nothing more than a browser built on the basis of free Chromium software (the Chrome engine) specially modified for web scraping. It is an easy tool to use and that implies that you should not have specific programming knowledge to start experimenting with it. It has many options although the freedom to program scraps of webscraping.io is greater.

It can be used by all types of users provided they are familiar with the basic concepts of the web world and with data visualization tools such as Excel and Google Spreadsheets.

Scrapy.org

It is a tool that works with the Python programming language. To use it, obviously, you have to have advanced programming knowledge in Python. From my point of view, it is a very complex tool to handle and not at all open to everyone. Another handicap is that if you want to use the extracted data to work with spreadsheets, the process will be complicated arithmetically.

It is a 100% tool designed for programmers with advanced knowledge of Python and for projects that do not require much work of visualization of data when working with the results of the ‘scrape’.

The web scraping as a substitute for an API

An API is a tool that allows us to exchange data between several websites. Let’s say that we have a sports newspaper and that we have a statistics section on football matches.

These data -in principle- are not filled in manually after each game. What sports newspapers usually do is connect to companies that have data centers. These companies are dedicated to giving access to this data through APIs. An API of this type will normally be a payment service.

With a web scraper programmed periodically, we can end up getting the same result to update the data on our website. In fact, the import.io tool already has a service that converts the web scraper into an API. Of course, unlike the API, it is not going to be a ‘real time’ process.

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Its use is very clear: we can take advantage of web scraping to get industrial quantities of information (Big data) without typing a single word. Through the search algorithms, we can track hundreds of websites to extract only the information we need. To do this, it will be very useful...