The Good Tech Companies - Mastering Scraped Data Management (AI Tips Inside)

Episode Date: November 21, 2024

This story was originally published on HackerNoon at: https://hackernoon.com/mastering-scraped-data-management-ai-tips-inside. Let's explore a few techniques to handle scraped data, including automatic data processing via AI. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #ai, #web-scraping, #data-science, #javascript, #web-development, #data-management, #good-company, and more. This story was written by: @brightdata. Learn more about this writer by checking @brightdata's about page, and for more stories, please visit hackernoon.com. Mastering scraped data involves more than extraction—it’s about cleaning, enriching, and exporting data effectively. From manual regex methods to AI-powered automation, this guide explores advanced processing techniques to handle even complex data sets. Export options include CSV, databases, and scalable formats like Protobuf or cloud storage.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Mastering Scraped Data Management, AI Tips Inside, by Bright Data. Red exclamation mark disclaimer. This is part 5 of our 6-part series on advanced web scraping. Just joining us, start with part 1 to catch up, grabbing data from a web page with HTML parsing is just the first step in a data management pipeline. You then need to prep that raw data for export so your teamwork company can actually extract value from it. Light bulb in this article, we'll explore the classic techniques alongside the latest and greatest innovations for automatic data processing and export of scraped data. Get ready to level up your data game. Graduation cab next steps after extracting data from a site.

If you've been following this six-part series on advanced web scraping, congratulations! You've leveled up your scraping skills to ninja status. Ninja here's a quick recap of what you've seen so far. 1. Prerequisites for building a powerful, modern web scraper. 2. How to retrieve data from spas, PWAs, and even AI-powered sites. 3. Tips and tricks to optimize your scraping workflows. 4. How to bypass rate limiters with AI-driven proxies. The bottom line is that your scraping script can tackle even the toughest modern sites,

effectively and efficiently extracting all their data. High voltage now that you have a treasure trove of data, the next steps are data processing, clean, enrich, and structure your data for export. Gear, data export, store your scraped data for future use in the right format. Inbox, let's break down these two final steps and show you how to go from raw scraped data to actionable insights, approaches to processing scraped data. Explore the most popular methods for both manual and automatic data processing. Manual data processing the concept is straightforward. Use custom regular expressions and trusty string manipulation methods like or other standard library functions to clean the data. And then, if needed, convert it into the

right data type. Broom, let's face it, you've probably done this before. So, it shouldn't be anything new. Face with monocle imagine you scraped this string from a product price. You want to extract the price number and currency. Here's how you might tackle it in JavaScript. Looks simple, right? But here's the problem. This kind of manual data cleaning works for most scraped pages.

It's not foolproof. Loudly crying so, manual data processing often requires logic to handle edge cases. Why? Because webpages evolve and can contain unique data, even if they're part of a specific page category. Light bulb pro tip. While manual optimization may get the job done, it's a bit old-school. The newest approach is to supercharge your pipeline with AI-based tools for automatic data processing. Automated data processing with AI-AI, especially LLMs, large language models, is revolutionizing data processing. These models excel at extracting clean, structured information from even the dirtiest, most chaotic, and noisy data. Why not leverage their power for web scraping? The idea here is to collect all your

raw data via web scraping and then pass it to AI to do the data cleaning for you. For example, consider the example below down finger here's the input string. Ask Chad GPT or any other LLM to extract the price and currency for you. The result? Just brilliant. Now imagine integrating the above logic directly into your scraper by calling an AI API, E, G, OpenAI, Anthropic, or other LLM providers. That would be avoiding all tedious custom cleaning logic and edge case debugging. Woman gesturing no gift bonus info.

AI isn't just about cleaning your data. It's also a powerful tool for enriching it. LLMs come with built-in knowledge that can add valuable data points or even fetch related info from other online sources. The only downsides with this approach, particularly if you opt for an open source AI models? Cost. While calling AI models hasn't an exorbitant price, it's not free either, especially at scale. Flying money. Data privacy. Sending your scraped data to a third-party AI provider can raise compliance issues. Lock. Best export methods for scraped data. Now that you've got data processing down, it's time to dive into exporting your data with some of the most effective methods.

Diving mask warning warning. While some export methods may sound familiar, don't bet a scourge, others might be more complex and a bit on the exotic side. Export to human-readable files exporting data to human-readable formats like CSV, JSON, or XML is a classic method for storing scraped data. How to achieve that? With a custom data export code at the end of your scraping script. Thumbs up pros. Easy to read and understand data formats. Universal compatibility with most tools, including Microsoft Excel, can be easily shared with non-technical users and used for manual inspection. Thumbs down cons. Limited scalability for large datasets. Old-fashioned approach to data export. Export to online databases redirecting scraped data

directly to online SQL or NoSQL databases, such as MySQL, PostgreSQL, or MongoDB databases. Thumbs up pros. Centralized access to scraped data. Supports complex querying • Easier integration with applications • Requires database setup and management • Potential writing performance issues with large volumes of data • Export to specialized big data formats • Storing scraped data in optimized formats like Protobuf, Parquet, Avro, and ORC, which are ideal for big data. Learn more about the differences between json and protobuf in the video below https colon slash www youtube com watch v equals ugyzn 6xkha and embeddable equals true thumbs up pros highly efficient in storage and retrieval. Great for large datasets with complex structures.

Supports schema evolution, thumbs down cons. Requires specialized tools for reading, as they are not human-readable. Not ideal for smaller datasets. Export to stream-compatible data files. Streamable formats like NDJSON and JSON lines allow for exporting data in a way that's efficient for real-time applications or processing. Thumbs up pros. Perfect for streaming and real-time processing. Supports large volumes of data efficiently. Flexible and scalable, in both reading and writing, while remaining human-readable. Thumbs down cons. Not all JSON libraries support them. Not so popular. Export to cloud storage providers

saving scraped data to cloud storage. Just like a WSS3 or Google Cloud Storage, offers easy, scalable, and accessible storage. Thumbs up pros. Unlimited scalability, especially in cloud-based web scraping. Easy access from anywhere. Low maintenance compared to physical storage. Thumbs down cons. Ongoing storage costs. Requires internet connection to access. Export via webhooks. Webhooks send data directly to external services in real-time, opening the door to immediate action or processing. Don't know what webhooks are? Watch this video. https colon slash slash www youtube com watch v equals mfzen4f9xk and embeddable equals true thumbs up pros immediate data delivery automates data transfer to external systems great for

integrations with third-party services for example via zapier are similar platforms thumbs down cons requires external service setup. Potential for data loss if service is down. How top companies process and handle scraped info. What's the best way to learn how to do something in the IT world? Look at what trusted developers, sources, or online providers are already doing. Light bulb and when it comes to top-tier data providers, Bright Data leads the pack. Trophy see what Bright Data's WebScraper API products offer for data processing and export. Bulk request handling to reduce server load and optimize high-volume scraping tasks.

Export data via WebHooker API delivery. Output data in formats like JSON, NDJSON, JSON lines, or CSV. Compliance with GDPR and CCPA for scraped data. Custom data validation rules to ensure reliability and save time on manual checks. Those features match all tips and tricks explored in this guide, and that's just scratching the surface of Bright Data's WebScraper API. Globe final thoughts, you've now mastered the most advanced techniques for managing scraped data, from processing to exporting like a pro. Hammer and wrench sure, you've picked up some serious tricks here, but the journey isn't over yet.

So, gear up and save your final burst of energy for what's next on this adventure. The final stop? Ethics and privacy compliance in web scraping, yes, even in a world where AI has rewritten the rules. Page facing up thank you for listening to this HackerNoon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.