In the digital age, data is one of the crucial and valuable insights. It brings us the latest insights, decisions, and technological innovations. Data crawling and data scraping are two major leading methods for gathering large data layers.
But what makes them so important, and how do they differ? Our blog post will break down these data collection models to help you understand their core differences and the best choice for your job board business growth.
What is Data Crawling?
Data crawling is a smart web crawling tool that automates the multiple processes of browsing to crawl the web to index content. Data crawlers, also popularly known as spiders or crawlers, implement a set of processes to systematically explore web pages to manage and collect large data sets.
It’s a robust and ideal method for search engines like Google, and Bing, which index (crawl) websites to serve powerful and unique search results. These common use cases include search engine indexing and large-scale enterprise data collection.
What is Data Scraping?
Data scraping involves precise and structured data extraction from a selective set of web pages. Unlike crawling, which processes and collects data from all pages of the source site, scraping targets specific data points from each page. Scraping ignores data not specifically targeted for collection.
This model is often used to gather scalable information for digital growth and analysis, such as job postings from top recruitment websites like LinkedIn, and Indeed. Data scraping reliably ensures the collection of data from destination sites and makes it easy to use for specific purposes through the structuring of the scrape output.
Key Differences Between Data Crawling and Data Scraping
Scope and Purpose
Data crawling focuses on indexing and managing large volumes of data sets. It provides a broad overview of web content, as in indiscriminately sucks up data and follows all possible links. Crawling collects the data but doesn’t make any sense of it – the data is amassed to be searched and surfaced and allow a follow-up of the specific details.
In contrast, data scraping engages in extracting specific pieces of information from selected pages. The output of a scrape is immediately useful as it is implicitly curated information that is then structured for specific uses – in the case of JobKapture, providing job posting inventory for job boards.
Technical Processes
Crawlers use simple architecture and navigation techniques to spider through web pages, following links to discover new sources of data and index their content in terms of levels of headings and information groupings.
Scrapers, on the other hand, access specific web page fields or values to extract specific and optimized information. Crawling involves exploring the web, while scraping is about retrieving specific data points.
Complexity and Tools
Crawling requires modern and next-gen tools like Apache Nutch, Elastic or Altova Scrapy to manage the complex and automated process. Scraping tools, such as JobKapture or Scrapy, are specifically designed and developed to scrape data from targeted source sites. Crawling configurations are often more complex and resource-consuming than scraping.
Ethical and Legal Considerations of Data Crawling and Scraping
Legality of Data Crawling
Data crawling must align with legal policies and channels, such as considering robots.txt files, which enable the page crawlers can access. Acceptable and efficient practices of algorithms include only crawling public data and not overloading web/data servers.
Legality of Data Scraping
Data scraping can have legal complexities, especially if it involves bypassing security measures such as a captcha, login page, or accessing restricted data from source sites. Ethical scraping means following best practices, significant resources, terms of service and only extracting public data.
Best Practices for Compliance
To ensure legal compliance, always adhere to website rules and robots.txt files. Avoid scraping sensitive, personal, financial, or confidential information. Follow limit rates to prevent overloading data servers and seek permission when it requires.
Use Cases or Examples of Data Crawling and Data Scraping
Use Cases of Data Crawling
Medium and enterprise organizations like Google and Microsoft use data crawling to crawl large datasets from the web and deliver filtered and relevant search results.
Academic researchers and data analytics firms use crawling to collect large datasets for analysis and lead generation purposes.
These examples highlight the advantages of broad data collection but also face risks and challenges in managing massive amounts of data/information.
Use Cases of Data Scraping
E-commerce companies, large business directories, and job boards scrape competitors’ structured data to adjust their own.
Job distributors or job board businesses scrape job postings to automatically keep their posting inventory updated or boost engagement of their job boards with targeted backfill inventory collected from top competitors.
While scraping provides valuable and optimized insights, it also requires a systematic process and data management to avoid legal issues and ensure data quality.
Choosing the Right Approach: Data Crawling or Data Scraping
Factors to Consider
Choose data crawling when you need an end-to-end and a large range of data, especially for use in combination with data mining or modelling. Opt for data scraping when you need specific information in a structured way from selected pages and websites. Consider your project objectives, KPIs, data needs, and available resources.
Combining Crawling and Scraping
In some cases, using both models can be cost-effective and reliable. For instance, crawl a set of websites to discover pages, then scrape filtered data from those pages. This integrated and well-defined strategy can maximize data collection efficiency.
Conclusion
Data crawling and data scraping are robust digital tools for gathering, structuring and sharing web data. Crawling provides a broad overview while scraping offers targeted insights.
Choosing the right model depends on your specific needs and business requirements. Both approaches are high-performing and efficient for leveraging the full potential of web data.
