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The web is loaded with digital data that can transform ideas, activities, and businesses. It is the source of information on customer trends, brand voice, and market share across any domain with a digital presence. This data helps to improve the basic functionality of teams and processes. Today, we have two popular ways to source public web data. One is through connecting API interfaces and the other is web scraping. Both these methods can deliver structured data, with which we can analyze insights and trends. However, the use cases for both these methods are contrasting, in terms of technical dependencies and benefits.
The process of web scraping is considered more like a passive role played by bots to extract content from websites. These bots are designed to pick on each data point of the webpage like text, tags, images, video titles, and subtags. The scrapers then store this information as a data file by structuring these elements. These data points can be extracted by installing browser extensions with readily available templates or approaching a data analytics provider. Whereas API requires both the website and the receiver to take active roles in automating the data pipeline
API often requires a custom-built application to target a specific part of the website for pulling out automated data, which requires a bit of technical expertise and manual effort.
Types of APIs
API can be simply defined as a contract to exchange data between two software products, defined by rules and agreements. In other words, it allows two products to communicate, where one app sends queries and the other pushes back a response with information or action. Not all websites allow API – application programming interface scraping. This type of scraping provides an interface to retrieve specific data from applications like websites and databases.
APIs work in two ways, one is by using URL endpoints and the other is through specific programming wrappers. For example, Twitter’s REST API allows receivers to access core data like searches and trends. So, basically, we can notice two types of APIs, Open and Internal. Where an open API is available for the public to mine data. The most common APIs that we might have come across will be Skyscanner for flight search, Yahoo Finance, and URL shortener.
Web Scraping API
Data is a valuable asset for organizations to smoothly run their businesses and make progressive data-backed decisions. Luckily, data scraping with API can overcome major hurdles like establishing a connection with dynamic websites and overcoming IP blocks, Captcha, and fingerprint scenarios. You must use an API because it is easy to integrate, customize, and saves time and cost. A custom application is built by defining a set of specifications and structure of responses in an XML or JSON format. Where this data is put to use for competitor monitoring, lead generation, and product optimization.
The Functionality of Web Scraping
Data analytics has become a huge part of how organizations are run. Rightly so, because data is growing exponentially with the world going digital and with a high surge in data consumption across the world. Web scraping is by far the most sophisticated way to get structured data. Either you can be familiar with big data analytics or work with one of the best web scraping providers like PromptCloud.
There are all kinds of data on the internet like images, product info, and customer reviews from Yelp or Tripadvisor; that can be used for market research. You might be interested to know Google regularly uses data analytics to index its content and for marketing purposes. Web scraping runs on three core principles. First, it makes an HTTP request to the server, extracts data by parsing the code, and it saves the relevant data on the cloud or locally.
The Role of an Analyst in Web Scraping
Now, we understand what web scraping does, but how does it function? How exactly does an analyst help to put things in perspective and give direction for bots to do their thing? An analyst provides the URLs for scraping by shortlisting the websites first. Then pushes a code to the scraper that identifies elements to be scraped on the front end. An analyst can also identify the source of data located in the backend of the website by using relevant nest tags. Once the nest tags are identified, with the help of Python libraries, the analyst specifies the data types like title or rating, to parse and save. A data analyst also keeps a check on if the target data is refined or not, if the terms of services are met, if the data protection protocols have been followed, or if the website is at risk of crashing. An analyst is always mindful of these considerations, and hence web scraping is easily one of the best ways to get structured data.
Conclusion
In simple terms, web scraping is extracting data from a website using data analysis software. API provides direct access to the data of an application. As a result, API can get a bit expensive. However, the primary goal for both is to access web data. But the approach taken in the modern era is companies preferring web scraping and API to extract data. But in most cases, a specialized web scraping company steps up to handle all your data scraping needs.
