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What Is Data Harvesting: Everything You Need to Know in 2024

January 15, 2024 · 5 min read

You can now find useful information in documents of all kinds of formats. Imagine collecting data from them and using it for your goals. Wouldn't that be great? Well, that's exactly what data harvesting is all about!

In this article, you will understand the meaning of harvesting data, what it brings to the table, and the best tools to do it. Let's dive in!

What Is Data Harvesting?

Data harvesting is the process of gathering information from one or more sources. These sources include web pages, text documents (e.g., PDFs, Word files), tabular files (e.g., spreadsheets, CSV files), existing data sets, and more.

In most cases, the desired data comes from public sites. In this scenario, data collection is called "web scraping" and involves getting data from web pages. Read our in-depth guide to learn more about what web scraping is and how it’s used.

Once collected, the data is aggregated, cleaned, and exported into easy-to-use formats. That way, it becomes accessible to as many people as possible in your team. Business users then use it for specific goals, like user profiling and decision-making.

As of 2024, harvesting data has become easier thanks to automated technologies and AI. It now encompasses online and local data retrieval, and even biometric data acquisition.

How Is Data Harvesting Used?

Data harvesting is a key step in tasks related to several industries and applications. Users of all types and expertise use it for different end goals. Here are the most common use cases broken down by user type:

  • Individuals: Tracking online prices for better deals, boosting productivity, and monitoring job openings. Retrying digital information can also enhance personal organization and efficiency in daily tasks.
  • Companies: Analyzing customer behavior, improving marketing strategies, and enhancing offerings. Businesses use data to make informed decisions, streamline operations, and gain a competitive edge.
  • Governments: Engaging in national security, formulating public policies, and allocating resources efficiently. Politicians use the retrieved data to assess public sentiment and adapt their work based on emerging social needs.

Harvesting data thus plays a key role in various fields, from the public to the private sector. For example, it supports medical research and personalized patient care. Also, marketers rely on it to monitor social media and study reactions to their campaigns.

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How to Do Data Harvesting

These are the general steps required to apply data harvesting:

  1. Identify data sources: Find relevant data sources for your specific goal. This could include websites, data sets, or repositories containing the desired information.
  2. Use a data extraction tool: Use a tool to simplify the process of extracting data from source documents. Depending on your needs, this may be a data parsing library, a no-code tool, or a desktop application. Note that automated tools make harvesting data faster and more accurate.
  3. Export data in a convenient format: After extracting the data, transform it into a format per your requirements. The most common formats for integration into analytical tools are CSV, XML, and JSON. You may also need to store the collected information in a database.

Let's consider a concrete example to understand better how this process works. Suppose you want to extract product data from Amazon with a Python script.

The sources will be the Amazon product pages of the selected items. The data extraction tool may be an HTML parsing library, such as Beautiful Soup. The export format implemented in Python logic could be CSV.

In particular, the automated Python script will:

  • Use an HTTP client to connect to the target page.
  • Feed the source HTML returned by the server to Beautiful Soup.
  • Select the HTML elements of interest and extract data from the parsed document.
  • Converted the scraped data to CSV format and exported it to an output file.

To learn how to build such a web scraper, see our guide on web scraping in Python.

How Do Websites Prevent Data Harvesting

Data is far more valuable nowadays than oil, and companies know it. That's why businesses aim to protect their data at all costs. No matter if it’s public information available on their site, they don't want users to steal it. Or at least, they want that to be as difficult as possible.

Some techniques sites adopt to prevent online data collection include IP bans, CAPTCHAs, fingerprinting, honeypots, and rate limiting. Learn more about these techniques in our article on web scraping challenges.

What do all these measures have in common? Blocking automated software that wants to access web pages for data retrieval. When an anti-bot system detects a request from a bot, it blocks it before it can extract the site's data.

Harvesting data from public online sources has become harder and harder. Thus, choosing the right tool for data extraction is now critical. Only tools that help you to avoid getting blocked guarantee efficient and effective results. It's time to take a look at the best data retrieval tool!

Best Data Harvesting Tool

When it comes to data harvesting, a large set of tools is available. These can be divided into two large categories based on their target audience:

  • For everyone: Browser extensions and desktop applications that allow data retrieval without code. While users of any skill level can use them, they typically come with many limitations. In particular, they tend to be error-prone and are likely to get detected by sites. Plus, they usually offer no customization at all.
  • For developers: Data parsing libraries that can extract data from several sources, such as HTML, CSV, and text documents. Advanced solutions offer ways to customize the requests and avoid bot detection.

No-code tools are great for basic data extraction but lack the flexibility needed for more complex tasks. You need to define custom scraping logic in automated scripts for a more reliable result.

Remember that a custom script isn't enough to build an effective data collection process. If you don't use the right tool, websites can still detect and block you.

Avoid all this with ZenRows, the definitive data collection tool every developer has always dreamed of. As a powerful web scraping API, it reduces online data retrieval to simple API calls. It also provides IP rotation via premium proxies and an AI-powered toolkit to elude any anti-bot system. A real game-changer!

Yes, data collection is legal. At the same time, you must abide by the rules set by the websites from which you collect data. To ensure ethical data collection practices, comply with their terms of service. Also, respect their robots.txt file.

Conclusion

This article explained the process of harvesting data from different sources. Now, you know:

  • What is data harvesting?
  • When to use it and what benefits it can bring.
  • Whether it's legal.
  • The most effective tools to perform it.

The other side of the coin is data protection technologies. These implement measures to block automated scripts from accessing data. Bypass them with ZenRows, a web scraping API with the most powerful anti-bot bypass capabilities on the market. Get the desired data from any online source with a single API call.

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