#data scraping api

[iwebscrapingblogs]

Uncover the hidden potential of data scraping to propel your startup's growth to new heights. Learn how data scraping can supercharge your startup's success.

[mostlysignssomeportents]

An interoperability rule for your money

This is the final weekend to back the Kickstarter campaign for the audiobook of my next novel, The Lost Cause. These kickstarters are how I pay my bills, which lets me publish my free essays nearly every day. If you enjoy my work, please consider backing!

"If you don't like it, why don't you take your business elsewhere?" It's the motto of the corporate apologist, someone so Hayek-pilled that they see every purchase as a ballot cast in the only election that matters – the one where you vote with your wallet.

Voting with your wallet is a pretty undignified way to go through life. For one thing, the people with the thickest wallets get the most votes, and for another, no matter who you vote for in that election, the Monopoly Party always wins, because that's the part of the thick-wallet set.

Contrary to the just-so fantasies of Milton-Friedman-poisoned bootlickers, there are plenty of reasons that one might stick with a business that one dislikes – even one that actively harms you.

The biggest reason for staying with a bad company is if they've figured out a way to punish you for leaving. Businesses are keenly attuned to ways to impose switching costs on disloyal customers. "Switching costs" are all the things you have to give up when you take your business elsewhere.

Businesses love high switching costs – think of your gym forcing you to pay to cancel your subscription or Apple turning off your groupchat checkmark when you switch to Android. The more it costs you to move to a rival vendor, the worse your existing vendor can treat you without worrying about losing your business.

Capitalists genuinely hate capitalism. As the FBI informant Peter Thiel says, "competition is for losers." The ideal 21st century "market" is something like Amazon, a platform that gets 45-51 cents out of every dollar earned by its sellers. Sure, those sellers all compete with one another, but no matter who wins, Amazon gets a cut:

https://pluralistic.net/2023/09/28/cloudalists/#cloud-capital

Think of how Facebook keeps users glued to its platform by making the price of leaving cutting of contact with your friends, family, communities and customers. Facebook tells its customers – advertisers – that people who hate the platform stick around because Facebook is so good at manipulating its users (this is a good sales pitch for a company that sells ads!). But there's a far simpler explanation for peoples' continued willingness to let Mark Zuckerberg spy on them: they hate Zuck, but they love their friends, so they stay:

https://www.eff.org/deeplinks/2021/08/facebooks-secret-war-switching-costs

One of the most important ways that regulators can help the public is by reducing switching costs. The easier it is for you to leave a company, the more likely it is they'll treat you well, and if they don't, you can walk away from them. That's just what the Consumer Finance Protection Bureau wants to do with its new Personal Financial Data Rights rule:

https://www.consumerfinance.gov/about-us/newsroom/cfpb-proposes-rule-to-jumpstart-competition-and-accelerate-shift-to-open-banking/

The new rule is aimed at banks, some of the rottenest businesses around. Remember when Wells Fargo ripped off millions of its customers by ordering its tellers to open fake accounts in their name, firing and blacklisting tellers who refused to break the law?

https://www.npr.org/sections/money/2016/10/07/497084491/episode-728-the-wells-fargo-hustle

While there are alternatives to banks – local credit unions are great – a lot of us end up with a bank by default and then struggle to switch, even though the banks give us progressively worse service, collectively rip us off for billions in junk fees, and even defraud us. But because the banks keep our data locked up, it can be hard to shop for better alternatives. And if we do go elsewhere, we're stuck with hours of tedious clerical work to replicate all our account data, payees, digital wallets, etc.

That's where the new CFPB order comes in: the Bureau will force banks to "share data at the person’s direction with other companies offering better products." So if you tell your bank to give your data to a competitor – or a comparison shopping site – it will have to do so…or else.

Banks often claim that they block account migration and comparison shopping sites because they want to protect their customers from ripoff artists. There are certainly plenty of ripoff artists (notwithstanding that some of them run banks). But banks have an irreconcilable conflict of interest here: they might want to stop (other) con-artists from robbing you, but they also want to make leaving as painful as possible.

Instead of letting shareholder-accountable bank execs in back rooms decide what the people you share your financial data are allowed to do with it, the CFPB is shouldering that responsibility, shifting those deliberations to the public activities of a democratically accountable agency. Under the new rule, the businesses you connect to your account data will be "prohibited from misusing or wrongfully monetizing the sensitive personal financial data."

This is an approach that my EFF colleague Bennett Cyphers and I first laid our in our 2021 paper, "Privacy Without Monopoly," where we describe how and why we should shift determinations about who is and isn't allowed to get your data from giant, monopolistic tech companies to democratic institutions, based on privacy law, not corporate whim:

https://www.eff.org/wp/interoperability-and-privacy

The new CFPB rule is aimed squarely at reducing switching costs. As CFPB Director Rohit Chopra says, "Today, we are proposing a rule to give consumers the power to walk away from bad service and choose the financial institutions that offer the best products and prices."

The rule bans banks from charging their customers junk fees to access their data, and bans businesses you give that data to from "collecting, using, or retaining data to advance their own commercial interests through actions like targeted or behavioral advertising." It also guarantees you the unrestricted right to revoke access to your data.

The rule is intended to replace the current state-of-the-art for data sharing, which is giving your banking password to third parties who go and scrape that data on your behalf. This is a tactic that comparison sites and financial dashboards have used since 2006, when Mint pioneered it:

https://www.eff.org/deeplinks/2019/12/mint-late-stage-adversarial-interoperability-demonstrates-what-we-had-and-what-we

A lot's happened since 2006. It's past time for American bank customers to have the right to access and share their data, so they can leave rotten banks and go to better ones.

The new rule is made possible by Section 1033 of the Consumer Financial Protection Act, which was passed in 2010. Chopra is one of the many Biden administrative appointees who have acquainted themselves with all the powers they already have, and then used those powers to help the American people:

https://pluralistic.net/2022/10/18/administrative-competence/#i-know-stuff

It's pretty wild that the first digital interoperability mandate is going to come from the CFPB, but it's also really cool. As Tim Wu demonstrated in 2021 when he wrote Biden's Executive Order on Promoting Competition in the American Economy, the administrative agencies have sweeping, grossly underutilized powers that can make a huge difference to everyday Americans' lives:

https://www.eff.org/de/deeplinks/2021/08/party-its-1979-og-antitrust-back-baby

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/10/21/let-my-dollars-go/#personal-financial-data-rights

My next novel is The Lost Cause, a hopeful novel of the climate emergency. Amazon won't sell the audiobook, so I made my own and I'm pre-selling it on Kickstarter!

Image: Steve Morgan (modified) https://commons.wikimedia.org/wiki/File:U.S._National_Bank_Building_-_Portland,_Oregon.jpg

Stefan Kühn (modified) https://commons.wikimedia.org/wiki/File:Abrissbirne.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

 - 

Rhys A. (modified) https://www.flickr.com/photos/rhysasplundh/5201859761/in/photostream/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[dkettchen]

not me finding out that fandom wikis do not have an api (which is like a specialised ui for programmers that a lot of websites have where you can more directly access resources off the website with programming tools, f.e. if one piece wiki had one, you could tell your coding languages like "hey get all the strawhat pirates' names and heights off their character pages and put them in my database here please" and it would be able to get that info off the wiki for you) so I will have to learn web scraping to get data off there for visualising ship stats project I have planned for my data analysis portfolio instead now smh

[3idatascraping]

How to Extract Amazon Product Prices Data with Python 3

Web data scraping assists in automating web scraping from websites. In this blog, we will create an Amazon product data scraper for scraping product prices and details. We will create this easy web extractor using SelectorLib and Python and run that in the console.

[foodspark-scraper]

Kroger Grocery Data Scraping | Kroger Grocery Data Extraction

Shopping Kroger grocery online has become very common these days. At Foodspark, we scrape Kroger grocery apps data online with our Kroger grocery data scraping API as well as also convert data to appropriate informational patterns and statistics.

[newsdataapi]

In today's world creating a website and application has become very easy with the use of APIs. With the APIs, you can integrate your website or applications with other sources. Here we have a list of Top Free APIs you can use in your APP development.

[scrapingbypass]

ScrapingBypass Web Scraping API Bypass Cloudflare Captcha Verification

ScrapingBypass API can bypass Cloudflare Captcha verification for web scraping using Python, Java, NodeJS, and Curl. $3 for 3-day trial: https://www.scrapingbypass.com/pricing ScrapingBypass: https://scrapingbypass.com Telegram: https://t.me/CloudBypassEN

[scrapingdog]

We scraped thousands of posts from popular subreddits to uncover real opinions, pros, and cons of moving to New York. Here's what the data tells us.

[mayurashinde]

Data Scraping Made Simple: What It Really Means

Data Scraping Made Simple: What It Really Means

In the digital world, data scraping is a powerful way to collect information from websites automatically. But what exactly does that mean—and why is it important?

Let’s break it down in simple terms.

What Is Data Scraping?

Data scraping (also called web scraping) is the process of using bots or scripts to extract data from websites. Instead of copying and pasting information manually, scraping tools do the job automatically—much faster and more efficiently.

You can scrape product prices, news headlines, job listings, real estate data, weather reports, and more.

Imagine visiting a website with hundreds of items. Now imagine a tool that can read all that content and save it in a spreadsheet in seconds. That’s what data scraping does.

Why Is It So Useful?

Businesses, researchers, and marketers use data scraping to:

Track competitors' prices

Monitor customer reviews

Gather contact info for leads

Collect news for trend analysis

Keep up with changing market data

In short, data scraping helps people get useful information without wasting time.

Is Data Scraping Legal?

It depends. Public data (like product prices or news articles) is usually okay to scrape, but private or copyrighted content is not. Always check a website’s terms of service before scraping it.

Tools for Data Scraping

There are many tools that make data scraping easy:

Beautiful Soup (for Python developers)

Octoparse (no coding needed)

Scrapy (for advanced scraping tasks)

SERPHouse APIs (for SEO and search engine data)

Some are code-based, others are point-and-click tools. Choose what suits your need and skill level.

Final Thoughts

What is data scraping? It’s the smart way to extract website content for business, research, or insights. With the right tools, it saves time, increases productivity, and opens up access to valuable online data.

Just remember: scrape responsibly.

[webdatacrawler0]

E-commerce Web Scraping | Data Scraping for eCommerce

Are you in need of data scraping for eCommerce industry? Get expert E-commerce web scraping services to extract real-time data. Flat 20%* off on ecommerce data scraping.

[iwebscrapingblogs]

Horse Racing Data Scraping | Scrape Horse Racing Data Daily

Horse racing, a sport steeped in tradition, continues to captivate audiences worldwide. Enthusiasts and bettors alike crave up-to-date information to make informed decisions. This is where horse racing data scraping comes into play. By leveraging modern technology, it's possible to scrape horse racing data daily, offering invaluable insights and a competitive edge. In this blog, we'll explore the intricacies of horse racing data scraping, its benefits, and how you can get started.

What is Horse Racing Data Scraping?

Data scraping involves extracting information from websites using automated tools. For horse racing, this means pulling data on races, horses, jockeys, track conditions, and more from various online sources. This information is then compiled into a structured format, such as a spreadsheet or database, where it can be easily analyzed.

Why Scrape Horse Racing Data?

Comprehensive Analysis: Scraping allows you to gather extensive data across multiple sources, providing a holistic view of the racing landscape. This includes historical performance, current form, and even predictive analytics.

Timeliness: Manually collecting data can be time-consuming and prone to errors. Automated scraping ensures you get the latest information daily, crucial for making timely betting decisions.

Competitive Edge: With access to detailed and up-to-date data, you can spot trends and patterns that others might miss. This can significantly improve your chances of placing successful bets.

Customization: Scraping allows you to collect data specific to your needs. Whether you're interested in particular races, horses, or statistics, you can tailor the scraping process to your preferences.

Key Data Points to Scrape

When setting up your horse racing data scraping project, focus on the following key data points:

Race Details: Date, time, location, race type, and distance.

Horse Information: Name, age, gender, breed, past performance, and current form.

Jockey Data: Name, weight, past performance, and win rates.

Trainer Statistics: Name, career statistics, recent performance, and track record.

Track Conditions: Weather, track surface, and condition ratings.

Betting Odds: Opening odds, closing odds, and fluctuations.

Tools and Techniques for Data Scraping

Python Libraries: Python offers several powerful libraries like BeautifulSoup, Scrapy, and Selenium for web scraping. BeautifulSoup is great for parsing HTML and XML documents, while Scrapy is a more robust framework for large-scale scraping projects. Selenium is useful for scraping dynamic content.

APIs: Some websites provide APIs (Application Programming Interfaces) that allow you to access their data directly. This is often a more reliable and ethical way to gather information.

Browser Extensions: Tools like Octoparse and ParseHub offer user-friendly interfaces for scraping without needing to write code. These are ideal for beginners or those who prefer a visual approach.

Database Management: Once data is scraped, tools like SQL databases or NoSQL databases (e.g., MongoDB) can help manage and analyze it effectively.

Ethical Considerations

It's important to approach data scraping ethically and legally. Here are some guidelines:

Respect Terms of Service: Always check the terms of service of the websites you plan to scrape. Some sites explicitly forbid scraping.

Rate Limiting: Avoid overwhelming a website's server with too many requests in a short period. Implement rate limiting to ensure your scraping activities don't cause disruptions.

Data Privacy: Be mindful of data privacy regulations and avoid scraping personal or sensitive information.

Getting Started

Identify Your Data Sources: Start by listing the websites and APIs that provide the data you need.

Choose Your Tools: Select the scraping tools that best fit your technical skills and project requirements.

Set Up Your Scraping Environment: Configure your development environment with the necessary libraries and tools.

Write and Test Your Scrapers: Develop your scraping scripts and test them to ensure they are extracting the correct data accurately.

Automate and Maintain: Set up automation to run your scrapers daily. Regularly monitor and update your scrapers to handle any changes in the websites' structures.

Conclusion

Horse racing data scraping offers a wealth of opportunities for enthusiasts and bettors to enhance their understanding and improve their betting strategies. By automating the data collection process, you can access timely, comprehensive, and accurate information, giving you a significant edge in the competitive world of horse racing. Whether you're a seasoned bettor or a newcomer, leveraging data scraping can take your horse racing experience to the next level.

[scrapin1]

The Future of Professional Networking: Exploring LinkedIn Scraping

In the digital age, the importance of professional networking cannot be overstated. LinkedIn, the premier platform for business and career networking, hosts millions of profiles and a plethora of company information. For businesses and individuals alike, accessing this wealth of data can offer significant advantages. This is where the concept of LinkedIn scraping comes into play, revolutionizing how we gather and utilize information.

Understanding LinkedIn Scraping

They refers to the process of extracting data from LinkedIn profiles and company pages using automated tools. This technique allows users to collect a wide range of data points such as job titles, skills, endorsements, company details, and much more. By automating the data collection process, scraping LinkedIn provides a more efficient and scalable way to gather crucial information compared to manual methods.

The Benefits of LinkedIn Scraping

The advantages ofLinkedIn data scrape are multifaceted, catering to various needs across different sectors:

1. Recruitment: For recruitment agencies and HR professionals, scraping LinkedIn can streamline the talent acquisition process. By extracting detailed profiles, recruiters can quickly identify and contact potential candidates that match specific job criteria.

2. Sales and Marketing: Sales teams can leverage scraping LinkedIn to build comprehensive lead lists. By targeting profiles that fit their ideal customer persona, businesses can enhance their outreach efforts and improve conversion rates.

3. Market Research: Companies conducting market research can use LinkedIn scraping to gather data on competitors, industry trends, and demographic information. This insight can inform strategic decisions and help businesses stay ahead of the curve.

Ethical and Legal Considerations

While LinkedIn scraping offers numerous benefits, it is crucial to navigate the ethical and legal landscape carefully. LinkedIn's terms of service explicitly prohibit unauthorized scraping of their data. Violating these terms can lead to legal repercussions and the banning of accounts. Therefore, it is essential to use compliant and ethical methods when performing LinkedIn scraping.

Introducing a Streamlined LinkedIn Scraper API

For those looking to implement LinkedIn scraping on a large scale, a streamlined LinkedIn scraper API is an invaluable tool. This API enables real-time data scraping of profiles and company information, providing up-to-date insights and information. By using such an API, businesses can efficiently gather and process data at scale without compromising on accuracy or speed.

Best Practices for LinkedIn Scraping

To ensure successful and compliant LinkedIn scraping, consider the following best practices:

1. Respect LinkedIn’s Terms of Service: Always adhere to LinkedIn’s guidelines to avoid potential legal issues. Use scraping tools that are designed to operate within these constraints.

2. Data Accuracy: Ensure that the scraping tool you use can accurately capture the necessary data points without errors. This reliability is crucial for maintaining the quality of your data.

3. Privacy Considerations: Be mindful of user privacy and data protection laws. Avoid scraping personal information that is not publicly available or necessary for your use case.

Conclusion:

LinkedIn scraping is transforming the way we access and utilize professional data. Whether for recruitment, sales, marketing, or research, the ability to extract and analyze LinkedIn data efficiently can provide a competitive edge. By using a streamlined LinkedIn scraper API, businesses can achieve real-time data scraping of profiles and company information at scale, ensuring they have the most current and relevant information at their fingertips. For those seeking a reliable solution,Scrapin.io offers a robust platform designed to meet these needs, enabling users to harness the full potential of LinkedIn data scraping while maintaining compliance and ethical standards.

Blog Source URL :

[3idatascraping]

Flight Price Monitoring Services | Scrape Airline Data

We Provide Flight Price Monitoring Services in USA, UK, Singapore, Italy, Canada, Spain and Australia and Extract or Scrape Airline Data from Online Airline / flight website and Mobile App like Booking, kayak, agoda.com, makemytrip, tripadvisor and Others.

[foodspark-scraper]

Tapping into Fresh Insights: Kroger Grocery Data Scraping

In today's data-driven world, the retail grocery industry is no exception when it comes to leveraging data for strategic decision-making. Kroger, one of the largest supermarket chains in the United States, offers a wealth of valuable data related to grocery products, pricing, customer preferences, and more. Extracting and harnessing this data through Kroger grocery data scraping can provide businesses and individuals with a competitive edge and valuable insights. This article explores the significance of grocery data extraction from Kroger, its benefits, and the methodologies involved.

The Power of Kroger Grocery Data

Kroger's extensive presence in the grocery market, both online and in physical stores, positions it as a significant source of data in the industry. This data is invaluable for a variety of stakeholders:

Kroger: The company can gain insights into customer buying patterns, product popularity, inventory management, and pricing strategies. This information empowers Kroger to optimize its product offerings and enhance the shopping experience.

Grocery Brands: Food manufacturers and brands can use Kroger's data to track product performance, assess market trends, and make informed decisions about product development and marketing strategies.

Consumers: Shoppers can benefit from Kroger's data by accessing information on product availability, pricing, and customer reviews, aiding in making informed purchasing decisions.

Benefits of Grocery Data Extraction from Kroger

Market Understanding: Extracted grocery data provides a deep understanding of the grocery retail market. Businesses can identify trends, competition, and areas for growth or diversification.

Product Optimization: Kroger and other retailers can optimize their product offerings by analyzing customer preferences, demand patterns, and pricing strategies. This data helps enhance inventory management and product selection.

Pricing Strategies: Monitoring pricing data from Kroger allows businesses to adjust their pricing strategies in response to market dynamics and competitor moves.

Inventory Management: Kroger grocery data extraction aids in managing inventory effectively, reducing waste, and improving supply chain operations.

Methodologies for Grocery Data Extraction from Kroger

To extract grocery data from Kroger, individuals and businesses can follow these methodologies:

Authorization: Ensure compliance with Kroger's terms of service and legal regulations. Authorization may be required for data extraction activities, and respecting privacy and copyright laws is essential.

Data Sources: Identify the specific data sources you wish to extract. Kroger's data encompasses product listings, pricing, customer reviews, and more.

Web Scraping Tools: Utilize web scraping tools, libraries, or custom scripts to extract data from Kroger's website. Common tools include Python libraries like BeautifulSoup and Scrapy.

Data Cleansing: Cleanse and structure the scraped data to make it usable for analysis. This may involve removing HTML tags, formatting data, and handling missing or inconsistent information.

Data Storage: Determine where and how to store the scraped data. Options include databases, spreadsheets, or cloud-based storage.

Data Analysis: Leverage data analysis tools and techniques to derive actionable insights from the scraped data. Visualization tools can help present findings effectively.

Ethical and Legal Compliance: Scrutinize ethical and legal considerations, including data privacy and copyright. Engage in responsible data extraction that aligns with ethical standards and regulations.

Scraping Frequency: Exercise caution regarding the frequency of scraping activities to prevent overloading Kroger's servers or causing disruptions.

Conclusion

Kroger grocery data scraping opens the door to fresh insights for businesses, brands, and consumers in the grocery retail industry. By harnessing Kroger's data, retailers can optimize their product offerings and pricing strategies, while consumers can make more informed shopping decisions. However, it is crucial to prioritize ethical and legal considerations, including compliance with Kroger's terms of service and data privacy regulations. In the dynamic landscape of grocery retail, data is the key to unlocking opportunities and staying competitive. Grocery data extraction from Kroger promises to deliver fresh perspectives and strategic advantages in this ever-evolving industry.

[webscreen-scraping]

Web Screen Scraping API offers robust customizable solutions for extracting data from any website, supports advanced features like crawling and handles high concurrency for optimal performance. With its flexibility and efficiency, it is used for several data scraping requirements.

[realdataapi01]

RealdataAPI is the one-stop solution for Web Scraper, Crawler, & Web Scraping APIs for Data Extraction in countries like USA, UK, UAE, Germany, Australia, etc.