Data Extraction API and How it Works
An Application Programming Interface (API) is essentially an interface that sits on top of some application and performs a certain function. Usually the technical jargon repulses someone from preparing personalized scripts for scraping and extracting. But with an API, you can do it on your system with an understandable and user-friendly interface that keeps you away from the complex coding and functioning of the process.
One of such services is data extraction where a customized API can perform automatic data extraction on articles, documents, dynamic webpages, blogs, and more. Usually, one would need to prepare scripts to extract data for each website and while this ensures customized results and quality, it is a huge compromise of speed and efficient labor for a large quantity of websites. As an alternative to that, automated applications including APIs come to the task with real-time and constant monitoring of any changes in the websites. APIs give flexibility for on-demand data extraction and one can integrate it with their existing applications and understand its working by reading the user-friendly documentation. Data Extraction using API will retrieve clean, structured data with only little human intervention and site-specific training.
An API helps in automating data extraction processes for all kind of documents scanned pdfs, images and for invoice extraction as well as for dedicated social media applications like Facebook or Twitter.
You compose a request for a profile or data using the Data Extraction API in compliance with REST (Representational State Transfer) standards. This request is a combination of parameters in the URL format.
The parameters include protocol, server, version, profile id and additional parameter values. The request is called a Uniform Resource Identifier (URI); an identifier used to access a resource on the internet. The URI would define the mechanism (or protocol) used to access the resource, the server and name of the resource on the server. The customization comes in this part; where the request is made up of a set of criteria to extract the data. It is the parameters that tell the request URL what you want from the webpage and the criteria you want to apply in the extraction such as frequency, quantity, range, sorting, location or data format.
Upon making a request with a URL, the receiving website will likely send back a response which can be the requested content or an error describing why the request failed. A response containing data will be in the format of HTML or XML or even JSON scripts which need cleaning for readability. Instead you can simply use the extractor API to extract certain data from the entire content such as some text, number, header tags and other response values.
If a single URL request can extract just one kind of information, it means that for every type of values we need we’ll have to provide a URL. For an extensive extraction, the list of URL grows rapidly. Thus, we have the GET method in HTTP to assign different values to parameters to extract different types of data.
As shown in the left image you can add GET parameters and a list of values to the base URL in the program to iteratively scrape those pages. Similarly, we have the POST method in HTTP whose parameters too send data to the website, but this data is used to access further pages; examples: filling a form, uploading details, logging in, etc.
Usually the POST parameters, like the ones shown in the image, contain confidential data which cannot appear publicly in the form of an URL like on the GET parameters. Additionally, POST methods are never cached, recorded or bookmarked like GET methods. With Data Extraction APIs, the functionality to enter and send POST parameters with your request is built-in and doesn’t require any additional coding. Additionally, you can use external applications or browser extensions for the same.
A good data extraction API will also take care of API keys, rate limiting and IP rotation for seamless and fruitful scraping.
Despite or because of such customization, the webpage may return an error message to your request; it may be a 404 or a 500. Nevertheless, the API will ignore the error and move on with the next URL since there’s no data to gather here. With pagination and concurrency control, the API is likely to be able to handle large number of requests and received data. And in case of any error or exception, there’s always the debugging bar to display what’s wrong.
Such a RESTful API for data extraction can smoothly perform human-like actions the web including retrying, pausing, and redirecting, and extract relevant data from all reachable corners of the web.