What Is Parsing of Data? Definition, Uses & Benefits

Access to relevant information, particularly when it comes in bulk, is critical to making correct business and analytical decisions. In areas like marketing research, financial analysis, competition monitoring, and even machine learning, data collection is of utmost importance. Since doing this process manually is not feasible, we employ automated techniques, one of which is data parsing.

This text aims to provide a comprehensive overview of what parsing is. Also, we will cover data parsing software and tools such as tailored and ready-made parsers.

What is data parsing?

This technique is used to retrieve materials from multiple sources such as websites, databases, or APIs. Most of the time, it is raw and full of other elements that do not facilitate its further use. Parsing offers a solution as it formats output in a more usable manner, making it convenient for further processes.

In a variety of domains, unorganized pieced together information is a common occurrence. Details located from different sources are highly likely to contain duplicates and irrelevant parts. Consider web scraping for instance, You would purchase it as a service to scrape and obtain relevant website content and in return, you would get cluttered HTML, advertisements, and unattractive navigation interfaces. The parser scans the text, eliminates unwanted and irrelevant parts, and organizes it in a more resistant manner.

That's what is parsing in programming scripts are useful:

• Business analytics – collected details can be uploaded into analysis systems and BI tools;
• Marketing – customer reviews, competitive company prices, and other relevant strategic data is analyzed;
• Machine learning – necessary information to set up the algorithm is gathered;
• Automation – updating databases of products and monitoring of news.

Therefore, we see that data parsing serves a different purpose, meaning it not only gathers the necessary captures, but also adds value to them by making them structured, usable and easy for further processes.

What does a parser do?

A parser’s workflow consists of a set of steps targeted at capturing relevant details to a specific need.

1. Defining parameters. The user specifies, in the detailed settings of the parser, all the addresses of web pages (or API addresses), files that contain information, or define selection criteria that will allow capturing essential elements, like prices, headlines, or product descriptions.
2. Source target visiting and structure analysis. The program will load the defined files or pages, analyze contents of the files, and later crawl to locate the required details. The parser can scan the HTML elements of the site, listen to useful events from dynamically generated JavaScript, or access the API.
3. Filtering and extracting. In performing parsing, the rules defined by the user are followed. For example, it discards irrelevant parts, executes processing of details, eliminating unnecessary spaces, special characters, and repetitions of text content.
4. Converting the data into usable forms. The extracted material is then processed and organized in accordance with the goals of the parsing. Saving in formats such as CSV, JSON, XML, or Excel is also possible.
5. Returning to the user or transferring to the system. Final parsing results can be provided to the user for his own examination or, depending on needs, uploaded into an analytical system to be interacted with more easily.

A parser may take the form of a script or a scraping software prepared to meet the particular nature of the task and the source. Depending on the needs, more general tools can be used, such as Octoparse or ParseHub, and more flexible ones for developers like Scrapy or BeautifulSoup.

That's an example on how to parse data from the European Central Bank through well-structured script. The purpose of this script is to gather details on currency exchange rates.

import requests  
from bs4 import BeautifulSoup  

# URL with currency exchange rates from the European Central Bank
url = "https://www.ecb.europa.eu/stats/eurofxref/eurofxref-daily.xml"  

# Send a GET request
response = requests.get(url)  

# Parse the XML response
soup = BeautifulSoup(response.content, "xml")  

# Find all  tags with currency and rate attributes  
currencies = soup.find_all("Cube", currency=True)  

# Display currency exchange rates
for currency in currencies:  
	name = currency["currency"]  # Currency code (USD, GBP, etc.)  
	value = currency["rate"]  # Exchange rate to the euro 
	print(f"{name}: {value} EUR")  

The script generates an automatic HTTP request to the official website of ECB, from which it downloads an XML document that contains exchange rates in Euros. BeautifulSoup is then used to parse the document, extracting the most relevant information and presenting it in a user-friendly manner.

Sample output:

USD: 1.0857 EUR  
GBP: 0.8579 EUR  
JPY: 162.48 EUR  

How we do it: Web Scraper API

API serves as an application interface in which multiple programs can exchange data through dedicated servers. HTML pages are instead parsed with information directly accessible in JSON, XML, or CSV formats.

Using this tool allows for faster and more accurate parsing by:

• Eliminating the impact of website design or structure on data collection.
• Improving processing speed by removing the need to search for elements within the HTML.
• Reducing the chance of account blocking due to submission of requests through designated official interfaces.
• Supporting integration with numerous systems including CRM, ERP, analytical systems, and automated reporting tools.

Classification of APIs for data extraction are as follows:

1. Open – are those without any restrictions and can be used to fetch information such as exchange rates, weather, or even coronavirus stats.
2. Private – these ones require an API key or authorization through rust or OAuth such as Google Maps API, Instagram, or Twitter.
3. Paid – these ones allow access for a fee or subscription, or place a cap on the number of requests such as SerpApi or RapidAPI.

Some services can be at the same time private and paid, like Google Maps which has an API key requirement and charges for the service.

APIs are the best data parsing tool option to use for services that are highly protected against web scraping, utilizing anti-bot devices, and request limits as well as authorization. It also allows you to legally work without the risk of blocking.

Additionally, it is the preferred choice when details has to be altered in real-time. For instance, traders and financial companies need to have constant access to the latest stock quotes while airline ticket prices are monitored by travel services.

Let us consider NewsAPI as an example. This is a service that takes information from a variety of places and compiles it into JSON format. News scraping is far from straightforward because websites have varied designs and anti-scraping measures are usually deployed. This service, however, provides an easy option to filter news articles using specific keywords, dates, and sources.

To extract details from NewsAPI:

1. First, the user registers on NewsAPI.org to obtain an API key which is required to make requests.
2. Use the command pip install requests to install the library.
3. Make a request and handle the response as provided in the code below:

import requests  

api_key = "YOUR_API_KEY"  
url = "https://newsapi.org/v2/everything"  

params = {  
	"q": "technology",  
	"language": "ru",  
	"sortBy": "publishedAt",  
	"apiKey": api_key  
}  

response = requests.get(url, params=params)  
data = response.json()  

# Display news headlines
for article in data["articles"]:  
	print(f"{article['title']} - {article['source']['name']}")  

What this code does:

1. Makes a request to NewsAPI, specifying keywords that should be included.
2. Waits for the structured data which arrives in JSON format.
3. Parses the returned information to get the headlines as well as the main sources.

A parsed response returns the titles of news articles, the name of the sources with the date and time when it was published. It may also contain a link to the main useful material, a description or the article's full text, as well as the category or topic pointer. Additionally, the response can include the author's name, tags, images, and other data.

Dedicated parser

Specialized parser is a tool used for particular source formats or information types. Unlike holistic solutions, these parsers are built for intricate structures, dynamically loaded content, and even for websites that are guarded against automated requests.

Specialized parses are used for scraping when:

• There are non-standard data structures in place which ordinary parsers will not be able to handle. For example, news sites that load content utilizing JavaScript code.
• Websites that implement protection against fraud by utilizing CAPTCHA systems, IP blocks, and require user authentication. Proxy servers, session control, and simulating user actions will help circumvent these barriers.
• Parsing of charts, tables, and bulky nested JSON structures responses is required. Such complex formats cannot be efficiently handled by universal parsers.
• Not only HTML code needs to be extracted, but also documents, pictures, videos, and audio files. In these situations, the parser has to be capable of OCR (optical character recognition) or conversion of the file.

Note. What is file parsing? File parsing is the approach of evaluating a file and obtaining information from it. It includes, but is not limited to, the reading of the file and transforming its content into a format suitable for anything from data processing to analysis.

Specialized tool guarantees simple and intuitive extraction of structured details from scanner-protected and complex resources. For instance, in this article, the reader will learn the aspects of setting up the specialized parser for scraping AliExpress.

Custom Parser

A custom parser is a tool designed for specialized tasks and business needs. This is built keeping in mind the data structure, update frequency, and the ability to work with other systems like CRM, ERP, or BI tools.

Custom scripts with specific parsers are appropriate when:

• It is required to scrape custom formats. For instance, when extracting price lists of competitors, only price and product attributes classifications have to be collected.
• There is a need to constantly and automatically process data without the need for human effort. This is crucial for businesses dealing with real-time updated information such as currency or product availability.
• Interoperability with other systems such as analytics, order management, and change detection is required. Custom configurations become a necessity in cases where simple off-the-shelf products do not configure to the required integration formats.
• It can only be extracted from an official API interface. At this point, a more stable and reliable method of information extraction is sought as opposed to regular web scraping.

The design of a custom parser provides maximum flexibility in adapting the information collection processes for business purposes and maximizes its efficiency and ease of use.

Usually, establishing a custom parser is more challenging than building a specialized one. It can be more reliable if it has some feature like request retries. This is important in the context of Python-based data parsing, especially when dealing with in constantly shifting environments. This approach permits resending requests, which helps with temporary server failures or blocks, and reduces the chances of losing information. One of the methods to solve this problem is the one presented in an article which concerns the problem of implementing repeated requests in Python. It studies basic and advanced retry patterns along with error coping mechanisms.

To comprehend the more fundamental distinctions between specialized and customized parsers, and the parsing each is best suited for, look at the table below.

Conclusion

Data parsing serves the purpose of rapidly gathering all kinds of details from diverse sources and transforming it into a usable format. Rather than physically searching for and copying it, the application itself fetches, collects, and organizes the needed information. There are different proprietary and bespoke parsers or user-friendly visual tools like Octoparse or ParseHub that can be used for this task. Depending on the kind of materials and specifics of the resource where it is found, the most appropriate choice is made. For integration with CRM, ERP, and other business tools, this is particularly advantageous and APIs eliminate a lot of the hassle involved in parsing data since they provide structured information devoid of HTML code, allowing for more straightforward systems integration.

Today, parsing remains an important aspect of business analytics, marketing, financial surveillance, and many other spheres. Companies that automate the collection of any materials definitely have an edge over their competitors because they are actively using real-time information which enables them to make informed and accurate decisions.