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What are the best practices for handling data acquisition challenges in web crawling?

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Data engineering is the process of designing, building, and maintaining data pipelines that collect, transform, and deliver data for various purposes. One of the common sources of data for data engineering projects is the web, where millions of websites contain valuable information that can be extracted and analyzed. However, web crawling, or the automated retrieval of web pages and their content, is not a straightforward task. It involves various challenges, such as dealing with dynamic content, handling errors and exceptions, respecting ethical and legal boundaries, and scaling up to large volumes of data. In this article, you will learn some of the best practices for handling data acquisition challenges in web crawling, and how to apply them to your own data engineering projects.

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Dave Cleminson - GAICD MDS

Consultant | Innovator | Enterprise Architect | Technology Strategy | Digital Twins | IoT | Data Scientist

1 Dynamic content

One of the main challenges of web crawling is that many websites use dynamic content, such as JavaScript, Ajax, or Flash, to generate and update their web pages. This means that the HTML source code that you see in your browser may not reflect the actual content that you want to crawl. To overcome this challenge, you need to use a web crawler that can execute and render dynamic content, such as Selenium, Scrapy-Splash, or Puppeteer. These tools allow you to simulate a real browser, interact with the web page elements, and capture the rendered HTML. However, they also require more resources and time than simple web crawlers, so you need to balance the trade-off between accuracy and efficiency.

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Dave Cleminson - GAICD MDS

Consultant | Innovator | Enterprise Architect | Technology Strategy | Digital Twins | IoT | Data Scientist
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If you’re having data acquisition problems, your strategy and planning either never happened or are flawed. The approach needs to be appropriately structured, to provide information and answers and/or insights based on the questions asked. From a visualisation perspective, the questions and insights are required to be defined and required data identified. Once these are understood, the structure can be developed catering for each source individually . Once these have been defined, they can be validated, implemented, and tested. If there are data issues, dynamic or static, The process should be repeated and reviewed to understand the flaws.

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Rahul S

Sr. Engineering Manager - Data at Xiaomi Technology | Ex-Amazon, Merck | Top Data Engineer Voice - Principal Architect - ?? Certified AWS Architect - Azure Cloud ? - SAFe?5 Agilist - Mentor - Hiring Data Engineers
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Web crawling, or web scraping, involves extracting data from websites for various purposes. However, several challenges can arise during the data acquisition process. Always check and respect the robots.txt file of a website. This file informs web crawlers about which parts of the site can or cannot be crawled. Ignoring this file may lead to legal issues and could result in being blocked. Implement a polite crawling strategy by setting appropriate delays between requests to avoid putting excessive load on the server. This helps prevent IP bans and ensures fair usage of resources. Rotate User-Agent strings in your HTTP requests to mimic different browsers or devices. Some websites may block or limit access based on the User-Agent

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2 Errors and exceptions

Another challenge of web crawling is that you may encounter various errors and exceptions while fetching web pages, such as network failures, server errors, timeouts, or redirects. These errors and exceptions can affect the quality and completeness of your data, and also waste your resources and bandwidth. To handle these issues, you need to implement robust error handling and retry mechanisms in your web crawler, such as logging, monitoring, backoff, and retry policies. These mechanisms allow you to track and resolve the errors and exceptions, and to retry the failed requests with appropriate intervals and limits. You also need to handle different types of HTTP status codes, such as 200 (OK), 301 (Moved Permanently), 404 (Not Found), or 503 (Service Unavailable), and act accordingly.

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Sourabh Joshi

LinkedIn Ghost-Writer | Owner at Sourabh AI passionate about AI and technology.
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In my experience, one of major issue is anti-bot measures. Most large e-commerce websites do not want to be scraped and use various security features. For example, websites add CAPTCHA challenges or even block IP addresses. Many budget scraping and crawling tools on the market are not efficient enough to gather data from large websites.

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3 Ethical and legal boundaries

Web crawling can be a challenge due to ethical and legal boundaries of websites. It is a form of data scraping, which is the extraction of data without the permission or consent of the owner, and can lead to privacy, copyright, or terms of service violations. To prevent these problems, web crawling etiquette rules should be followed. For example, check the robots.txt file of the website to see what rules and permissions are specified for web crawlers. Additionally, read the terms and conditions of the website to make sure web crawling or data scraping are not prohibited or limited. Furthermore, send a user-agent header with your web crawler that identifies its purpose and contact information. Additionally, limit the frequency and concurrency of your web crawler to reduce the load and impact on the website server. Finally, if possible, ask for permission or consent from the website owner before web crawling or data scraping.

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4 Scaling up

When web crawling, you may need to scale up your web crawler to handle large volumes of data or complex web structures. This can be a resource-intensive and time-consuming process, so it’s important to use some techniques to increase efficiency. Parallelizing and distributing your web crawler allows you to run multiple instances on different machines or threads and coordinate them with a message queue or database. A proxy service or VPN can also help you change your IP address and avoid being blocked or throttled by the website server. Additionally, using a cloud service or platform such as AWS, Google Cloud, or Scrapinghub provides scalable and reliable infrastructure and tools for web crawling and data extraction.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for handling data acquisition challenges in web crawling?

1

2

3

4

5

1 Dynamic content

2 Errors and exceptions

3 Ethical and legal boundaries

4 Scaling up

5 Here’s what else to consider

Data Engineering

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What are the best practices for handling data acquisition challenges in web crawling?

1

2

3

4

5

1 Dynamic content

2 Errors and exceptions

3 Ethical and legal boundaries

4 Scaling up

5 Here’s what else to consider

Data Engineering

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