As we established in other installments of our scraping series, collecting, collating, and managing the vast amounts of data inherent with the modern internet is no easy task. As such, scraping has exploded in popularity. So far, we’ve covered web and screen scraping; in this guide, we’ll cover a much broader term: data scraping.
What is Data Scraping?
Data scraping refers to automatically extracting information or data from various sources, not necessarily limited to web or user interface content. Data scraping can involve collecting data from various sources, including databases, documents, spreadsheets, text files, and other structured or unstructured data repositories. It is an umbrella term under which both web and screen scraping fall.
What Distinguishes Data Scraping, Web Scraping, and Screen Scraping from One Another?
- Data Source: Data scraping is a broad term that refers to the extraction of data from various sources, not limited to web-based content. It can include databases, documents, spreadsheets, text files, APIs, and more.
Method: Data scraping can involve automated techniques for extracting structured or unstructured data from a variety of sources. This process can be used for data integration, migration, analysis, and information retrieval from diverse data repositories.
- Data Source: Web scraping specifically focuses on extracting data from websites and web pages. It is primarily used for collecting information from web content, such as text, images, links, and other elements found on websites.
- Method: Web scraping usually involves automated techniques to fetch and parse the HTML code of web pages, extracting data from the page's content. It is often used for purposes like data aggregation, price monitoring, content curation, and competitive analysis.
- Data Source: Screen scraping refers to the process of capturing data from the visual display of computer screens. It is typically used with desktop or legacy applications where the data is not easily accessible in a structured digital format.
- Method: In screen scraping, software tools or scripts interact with the user interface of applications to capture and extract data from the screen. This may involve capturing text, images, or other visual elements, and then processing that data. Screen scraping is often used to integrate data from legacy systems into modern applications.
What is the Difference Between Data Scraping and Data Crawling?
Data scraping involves extracting specific data from websites or other online sources. It focuses on retrieving particular pieces of data, such as product prices, contact information, or news articles. It is a narrower and more targeted process than data crawling and seeks to capture specific data elements from web pages, like extracting product names and prices from an e-commerce site. Data scraping is typically performed on-demand or periodically to collect the latest data. It is often initiated by a user or a script when there's a need for specific data. Data scraping is most used for price monitoring, content aggregation, sentiment analysis, or competitive analysis.
However, data crawling, often called web crawling or indexing, involves systematically browsing the web to index or catalog web pages and their content. The primary goal is to discover and categorize web pages. It is a broader process that seeks to cover a wide range of web pages and their links, creating a map or index of the web's content. Data crawling is a continuous or periodic process, with search engines like Google crawling the web constantly to keep their indexes up to date. It involves systematically following links from one web page to another, discovering new pages, and updating or re-indexing known pages. It often uses algorithms like depth-first or breadth-first traversal.
How Does Data Scraping Work?
Data scraping works by automating the process of retrieving and extracting data from various sources, such as websites, databases, documents, or structured or unstructured data repositories. Here's a general overview of how data scraping works:
Select Data Source: First, you identify the data source from which you want to extract information; this could be a specific website, a database, a document, or any other data repository.
Access the Data: If the data source is a website, data scraping begins by sending HTTP requests to the website's server to retrieve the web page's HTML content. You might need to establish a connection using the appropriate protocols or APIs for other sources like databases.
Select and Extract Data: Once the HTML is parsed, you define criteria to identify and extract the specific data you need; this involves using selectors like CSS selectors or XPath to target the HTML elements that contain the data of interest. For instance, you could extract product prices, names, and descriptions from an e-commerce website.
Data Cleaning and Transformation: Raw data from scraping may contain unwanted elements, inconsistencies, or formatting issues. Data cleaning involves removing or handling these anomalies to make the data usable, including removing HTML tags, converting data to a standardized format, and handling missing values.
Storage and Export: After scraping and cleaning the data, you can choose to store it in a database, a spreadsheet, a text file, or any other suitable storage medium. Some scraping projects may involve real-time analysis or direct integration with other systems, so the data may not be stored locally.
Automation and Scheduling: Data scraping is often automated using scripts or specialized tools. You can schedule scraping tasks at specific intervals or in response to certain events; this allows for continuous data collection or periodic updates.
Monitoring and Error Handling: Data scraping processes often need monitoring to ensure they run as expected. You should implement error-handling mechanisms to address issues that might arise, such as changes in the source's structure or interruptions in the scraping process.
Scalability: For larger data scraping projects, consider scaling your infrastructure to handle a high volume of data or frequent updates; this could involve distributed computing, proxies, and more advanced techniques.
However, techniques differ for different tools. For example, Fortra’s Automate, a no-code robotic process automation solution, provides pre-built native actions, an API integration kit, and a UI recorder to help navigate applications or websites, allowing users to identify and extract data based on element attributes or patterns, and finally, process, transform, or store parsed data as per the workflow.
How to Scrape Data with Fortra’s Automate
Automate can quickly and easily scrape data from multiple source types including databases, documents, spreadsheets, and more. In this example, you’ll see how to build an automated workflow to scrape data from PDF invoices and a web portal using Automate’s native actions for OCR and web, and then import that data into a SQL database.
Data Scraping Use Cases and Benefits
Data scraping offers a range of benefits for businesses, researchers, and individuals in various industries. Perhaps most importantly, it dramatically increases business efficiency, automating the data collection process and allowing for the quick and efficient extraction of large volumes of data from various sources.
It can also provide real-time or near-real-time data updates, ensuring you always have access to the most current information; this is crucial for applications like stock market tracking and social media monitoring. The real-time data you have collected can then be integrated into a centralized database or system, making it easier to analyze, compare, and derive insights from diverse datasets.
Businesses scrape data to monitor competitors, track market trends, and adjust strategies in response to changing conditions, giving them a competitive edge. Similarly, automating data collection reduces the need for manual data entry, saving time and labor costs. It can also help avoid errors associated with manual data entry.
Possible use cases for data scraping include:
Data Scraping Considerations
Before embarking on a data scraping project, it’s important to keep the following considerations in mind:
Legal and Ethical Issues:
It’s essential to confirm whether you have the necessary rights and access to scrape data before you do so. Best practice is to check the website’s scraping policies.
Excessive scraping requests can overwhelm the source server. Try to limit requests where possible.
You must determine where and how data will be stored before you scrape it.
Ensure any personal data is handled according to data privacy regulations. These requirements will differ depending on your location and industry, so be sure to determine which ones apply to you.
Websites and data structures change, so ensure the scraper is maintainable
How Data Scraping Software Can Assist
Fortra’s Automate tool helps modern businesses access crucial data regardless of its location, structure, or scale. It eradicates the need for your staff to spend valuable time creating custom processing and filtering algorithms, writing scripts, or utilizing another tool to integrate scraped data into your IT infrastructure. With over 70 native actions to common applications like Microsoft, FTP, AWS, OCR, and more, plus a powerful API integration kit, Automate makes it easy to integrate with the key systems and applications involved in your data movement and transformation processes.
Automate uses a unique mix of approaches to capture data based on your needs—from optical character recognition to machine learning and screen recording. Automate can extract data from almost any source, send data directly into your documents, reports, or other business systems, bridging the gap between unstructured data and business applications. It improves productivity, reduces errors, optimizes resources, and increases efficiency.
To learn more, check out this on demand webinar on automated data scraping and extraction for web and more.
Data scraping is a vital technique for extracting data from diverse sources, offering efficiency, real-time updates, and numerous applications across various industries. It automates the collection of large volumes of data from various sources, providing real-time updates that can be integrated into a centralized database, enabling easier analysis and insights. It also helps monitor competitors, track market trends, adjust business strategies, and saves time and labor costs by reducing manual data entry and minimizing errors. However, legal, ethical, and technical considerations are crucial in ensuring successful and responsible data scraping projects. Be sure to refer to this guide when embarking on your scraping project.
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