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Bright Data is the world’s largest data collection platform dedicated to helping all businesses view the Internet just like their consumers and potential consumers do each and every day. We help global brands gather publicly available web data in an ethical manner and transform unstructured data (e.g., HTML) into structured data (e.g., xl). Working with over 15,000 customers, including market-leaders from the Fortune 500 space, the company...

Bright Data | LinkedIn

Bright Data may be growing as the company has been actively involved in revealing insights into market trends, such as the most popular purchases in the gifting economy. This indicates that Bright Data is not only collecting but also analyzing large datasets, which is a core function of a web ...

Bright Data - Crunchbase Company Profile & Funding

The company was founded under the ... Hola VPN company in 2014. In 2017, Luminati Networks was sold separately from Hola to EMK Capital, a private investment fund from London, at a value of approximately 200 million dollars. In March 2021, Luminati Networks was renamed to Bright Data...

Bright Data - Wikipedia

Working with thousands of customers, including market leaders from the Fortune 500 list, Bright Data provides brands with a transparent view of the internet. The company&#x27;s data collection automation platform enables clients to view the internet through various points in its global network, ...

Bright Data - Open-source Data Collection | Startup Nation Finder

Bright Data is a company that specializes in web data services within the technology industry. Use the CB Insights Platform to explore Bright Data&#x27;s full profile.

Bright Data - Products, Competitors, Financials, Employees, Headquarters Locations

Learn how Bright Data helps the best companies in the world collect web data without being blocked or misled. Ethically. Quickly. Accurately.

About Bright Data

LONDON--(BUSINESS WIRE)--Luminati Networks has today announced it is to become Bright Data as it takes the next step in its journey to deliver valuable online data to organisations and further support the data collection industry. Since its foundation in 2014, the company has experienced ...

Luminati Networks Becomes Bright Data with Focus on Web Transparency and Continuous Innovation | Business Wire

Information on valuation, funding, acquisitions, investors, and executives for Bright Data. Use the PitchBook Platform to explore the full profile.

Bright Data Company Profile: Funding & Investors | PitchBook

I have a travel agency and I needed to investigate travel offers of my competitors with the offers of my company. Bright Data collected all required data with great accuracy and speed and this helped me to create a winning strategy for my business. Very thankful for their full assist for my case.”

Bright Data - Pricing, Reviews, Data & APIs | Datarade

Bright Data - Facebook

Bright Data has 1 employees across 5 locations. See insights on Bright Data including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Bright Data Company Profile - Office Locations, Competitors, Revenue, Financials, Employees, Key People, Subsidiaries | Craft.co

In other words, Meta was a customer of Bright Data’s web-scraping services before it went to sue them, though it used the company’s services for a different purpose. In a related matter, Bright Data had also been accused of collecting personal information about minors pulled from Facebook ...

Court rules in favor of a web scraper, Bright Data, which Meta had used and then sued | TechCrunch

Bright Data - Cloud based data collection solution provider. from 1 investor.

Bright Data - Company Profile - Tracxn

View Bright Data (brightdata.com) location in Central District, Israel , revenue, industry and description. Find related and similar companies as well as employees by title and much more.

Bright Data - Overview, News & Similar companies

BRIGHT DATA LTD - Free company information from Companies House including registered office address, filing history, accounts, annual return, officers, charges, business activity

BRIGHT DATA LTD overview - Find and update company information - GOV.UK

We distribute Internet number resources to our members and provide tools to help them manage their allocations and assignments · More information about Manage IPs and ASNs

Bright Data Limited

Need residential proxies? The company has 72 million shared and exclusive IPs across 195 countries. They&#x27;re sourced from user devices, but if you&#x27;re looking for more reliability and speed, Bright Data also has 700,000 proxies sourced directly from ISPs.

Bright Data review | TechRadar

Or Lenchner’s path to becoming CEO of Bright Data included cleaning toilets, dropping out of school, and telling a company’s founder he did not like its product

The bold email that led a young surfer to a CEO role - CTech

X Corp., the company formerly known as Twitter, is suing Israel-based web data collection company Bright Data Ltd. over alleged unauthorized scraping and selling of content and user data pulled from the social media platform.

Musk's X Corp sues Israel's Bright Data for scraping data | The Times of Israel

From data collection to ready-made datasets, Bright Data allows you to retrieve the data that matters

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The Noonification: Meet Bright Data: HackerNoon Company of the Week (7/22/2024)

Another week, another Company of the Week! Every week, we share an awesome tech brand from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we are proud to present Bright Data - one of the largest data collection platform on the planet internet having more than 15,000 customers worldwide with aim to provide web transparency, and equal access to open-source, web-based information. Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon! Meet Bright Data: #FunFact Do you know that back in 2014, the company launched with the name of Luminati Network? Here’s why they rebranded later: “When the company was first established back in 2014, the goal was (and still remains) to provide web transparency, and equal access to open-source, web-based information. Shedding light on the ‘dark corners’ of the web, and being a ‘luminary’, a natural light-giving body like a sun or a moon. Eventually leading the founders to the name ‘Luminati’. But what they learned during the following years was that ‘Luminati’ had pre-existing negative associations that would prove hard to shake off from a reputational standpoint. For some people, Luminati evoked clandestine ‘Illuminati’ brotherhood associations. Something that stood for the exact opposite of what the company was trying to accomplish (e.g. secrecy, and access to information reserved for the elites).To reiterate this is the OPPOSITE of what Luminati is/was trying to achieve:

Transparency
A belief that web-based information is like water, a natural resource that belongs and is accessible to everyone
Promoting equality, free trade, and accountability” Hence, the name ‘Bright Data!’ Bright Data’s Top Stories on HackerNoon Bright Data has published 9 stories via the HackerNoon Business Blogging Program, and has generated 20 days, 8 hours, and 29 minutes of reading time! Here are our top 3 favorite stories by them: How to Stay Anonymous Online by Hiding Your IP Address Alternatives to Web Scraping with Python Cracking the Code of Cloudflare Bypass Share Your Company's Story via HackerNoon That's all this week, folks! Stay Creative, Stay Iconic. The HackerNoon Team Another week, another Company of the Week ! Every week, we share an awesome tech brand from our tech company database , making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. Company of the Week tech company database This week, we are proud to present Bright Data - one of the largest data collection platform on the planet internet having more than 15,000 customers worldwide with aim to provide web transparency, and equal access to open-source, web-based information. Bright Data Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon ! Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Meet Bright Data: #FunFact Do you know that back in 2014, the company launched with the name of Luminati Network? Here’s why they rebranded later : rebranded later “When the company was first established back in 2014, the goal was (and still remains) to provide web transparency, and equal access to open-source, web-based information. Shedding light on the ‘dark corners’ of the web, and being a ‘luminary’, a natural light-giving body like a sun or a moon. Eventually leading the founders to the name ‘Luminati’. But what they learned during the following years was that ‘Luminati’ had pre-existing negative associations that would prove hard to shake off from a reputational standpoint. For some people, Luminati evoked clandestine ‘Illuminati’ brotherhood associations. Something that stood for the exact opposite of what the company was trying to accomplish (e.g. secrecy, and access to information reserved for the elites).To reiterate this is the OPPOSITE of what Luminati is/was trying to achieve: Transparency
A belief that web-based information is like water, a natural resource that belongs and is accessible to everyone
Promoting equality, free trade, and accountability” “When the company was first established back in 2014, the goal was (and still remains) to provide web transparency, and equal access to open-source, web-based information. Shedding light on the ‘dark corners’ of the web, and being a ‘luminary’, a natural light-giving body like a sun or a moon. Eventually leading the founders to the name ‘Luminati’. But what they learned during the following years was that ‘Luminati’ had pre-existing negative associations that would prove hard to shake off from a reputational standpoint. For some people, Luminati evoked clandestine ‘Illuminati’ brotherhood associations. Something that stood for the exact opposite of what the company was trying to accomplish (e.g. secrecy, and access to information reserved for the elites).To reiterate this is the OPPOSITE of what Luminati is/was trying to achieve: Transparency A belief that web-based information is like water, a natural resource that belongs and is accessible to everyone Promoting equality, free trade, and accountability” Transparency A belief that web-based information is like water, a natural resource that belongs and is accessible to everyone Promoting equality, free trade, and accountability” Hence, the name ‘Bright Data!’ Bright Data’s Top Stories on HackerNoon Bright Data has published 9 stories via the HackerNoon Business Blogging Program , and has generated 20 days, 8 hours, and 29 minutes of reading time! Here are our top 3 favorite stories by them: HackerNoon Business Blogging Program How to Stay Anonymous Online by Hiding Your IP Address How to Stay Anonymous Online by Hiding Your IP Address How to Stay Anonymous Online by Hiding Your IP Address Alternatives to Web Scraping with Python Alternatives to Web Scraping with Python Alternatives to Web Scraping with Python Cracking the Code of Cloudflare Bypass Cracking the Code of Cloudflare Bypass Cracking the Code of Cloudflare Bypass Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon That's all this week, folks! Stay Creative, Stay Iconic. The HackerNoon Team

Meet Bright Data: HackerNoon Company of the Week

মিট ব্রাইট ডেটা: হ্যাকারনুন কোম্পানি অফ দ্য উইক

Lernen Sie Bright Data kennen: HackerNoon-Unternehmen der Woche

Conozca Bright Data: la empresa de la semana de HackerNoon

Rencontrez Bright Data : Entreprise HackerNoon de la semaine

ब्राइट डेटा से मिलिए: हैकरनून कंपनी ऑफ द वीक

Bright Data のご紹介: HackerNoon の今週の企業

밝은 데이터를 만나보세요: 금주의 HackerNoon 회사

Conheça Bright Data: Empresa HackerNoon da Semana

Встречайте Bright Data: компания недели HackerNoon

Bright Data ile Tanışın: Haftanın HackerNoon Şirketi

Gặp gỡ Bright Data: Công ty HackerNoon của tuần

结识 Bright Data：本周 HackerNoon 公司

You are reading the third part of a three-part series. It is highly encouraged to that you read the previous two articles before proceeding with this part! Flix-Finder [Part 1/3] Flix-Finder [Part 2/3] By the end of the previous blog, Flix-Finder supported movie searches based on rating parameters from various agencies. The functionality of our app met expectations and achieved its intended function. But after a while, Flix-Finder's customers began to gripe that the app did not provide the most recent information about films. Because Flix-Finder employs static data, this is something we anticipate. Flix-Finder needs some process through which its movie data can be updated to reflect the new movies. And, this is something we would be implementing in this final part! Despite the fact that there are several ways to implement this functionality, we'll be creating a new endpoint that can be used to update the data. Your data would change instantaneously, much as when you press a button. But there are other strategies as well, such utilising a cronjob. Once the specifics of one strategy are described in this blog, any new technique may be easily created using the same concepts. Planning it all out… Before we start to code, let us take a step back and understand all our requirements. We want to create a new endpoint and hitting that endpoint should update the data Flix-Finder uses. Let us use endpoint and any hitting this endpoint should update the data our app uses. /update GET request With the interface planned out, let us now think about ‘ . ‘ How to update the data? The process of updating the data can be broken into three steps: (1) Re-Run the Collectors on Bright Data (2) Fetch the Results obtained from running the collector (3) Overwrite the results to static files For the first two steps, we can use the APIs provided by Bright Data. Once we have the data, we can use vanilla Python to overwrite the existing static files. Great! With everything planned out, let us start the implementation. Adding the new Endpoint to Flix-Finder Start by creating a new method inside the file of recommender app in . We will call this method . This is where all our logic of updating the static data will reside. view.py Django updateData # recommender/views.py

def update(request):
 # TODO: Implement this method
 return HttpResponse("Movie Data Updated!") Let us set up our views as well. We need to add the mapping for the newly created method in views. # recommender/urls.py

from django.urls import path

from . import views

urlpatterns = [
 path("", views.index, name = "index"),
 path("update", views.updateData, name = "update data")
] With this, we have successfully created a new endpoint in Flix-Finder. We will be using this endpoint to update the static data. For now, this endpoint does not do anything and simply returns a String message. ➜ ~ curl localhost:8000/update 
Movie Data Updated! Generating API Token on Bright Data Before we can start using Bright Data’s API for data update, we need to generate an API Token. All communications to Bright Data should contain this token and it is used for authentication purposes. Steps to generate API Token : Head over to . https://brightdata.com/cp/setting In the API Token section, click on button. Add Token In the dialog box, select the user, the permission and the expiration date. Click on the save button and enter the 2FA code. Copy and securely keep the generated token. Make sure to not leak your API Token. Anyone with access to your API Token can literally mimic you on Bright Data!!! Once the API Token is generated, we are ready to start using the APIs for running the collectors and fetching data from collector runs. APIs for Initiating Collectors Head over to Initiate by API section of your collectors. Here you will find curl requests that you can use to re-run collectors and fetch the results. Bright Data provides an endpoint to initiate collector run. The endpoint expects a POST request. It also expects a query parameter ‘ ’ that is used to uniquely identify the collector. The header should contain the API token. API to initiate collector run: https://api.brightdata.com/dca/trigger_immediate collector Here is the curl request for the same. curl -H "Authorization: Bearer API_TOKEN" -H "Content-Type: application/json" -d '{}' "https://api.brightdata.com/dca/trigger_immediate?collector=c_rj3i0wspnhr0ataba" The response of this endpoint is a response ID. This can be used to later fetch the results. With the response ID obtained from running a collector, Bright Data provides us with another API to fetch results from the collector run. The endpoint expects a GET request. It also expects a query parameter ‘response_id’ that is used to uniquely identify the collector. The header should contain the API token. API to fetch results from a collector’s run: https://api.brightdata.com/dca/get_result Here is how the curl request for this endpoint would look. curl "https://api.brightdata.com/dca/get_result?response_id=RESPONSE_ID" -H "Authorization: Bearer API_TOKEN" Implementing the update endpoint With the knowledge of endpoints to use and API token handy, we can start implementing the update endpoint. Let us implement this endpoint according to the subtasks we identified initially. (1) Re-Run the Collectors on Bright Data (2) Fetch the Results obtained from running the collector (3) Overwrite the results to static files First we need to use the re-run collector API and get a response ID. We will be using Python’s requests library to perform this task. API_TOKEN = "###########" # Replace by your API Token
response = requests.post(
 "https://api.brightdata.com/dca/trigger_immediate?collector=c_rj3i0wspnhr0ataba",
 headers={"Authorization": f"Bearer {API_TOKEN}", "Content-Type": "application/json"},
 json={}
)
if response.status_code < 200 or response.status_code > 299 or 'response_id' not in response.json():
 return HttpResponseServerError('Something Wrong Happened')
response_id = response.json().get('response_id') Next step is to use the response ID to fetch the results. We would wait for some time before trying to fetch the results. time.sleep(30) # Sleeping for 30 seconds
get_results_response = requests.get(
 f"https://api.brightdata.com/dca/get_result?response_id={response_id}",
 headers={"Authorization": f"Bearer {API_TOKEN}"}
) With data in hand, we can overwrite the static file with this new data. movie_data = get_results_response.json()
data_file = open(ROTTEN_TOMATO_DATA_FILE_PATH, encoding="utf8", mode="w")
json.dump(movie_data, data_file, indent=4)
data_file.close() With that the implementation of the update method is completed. Using the new endpoint A simple curl request can be made to the endpoint to initiate the data updation process. Once the API returns, the data would have been updated. localhost:8000/update ➜ ~ curl localhost:8000/update 
Movie Data Updated! Concluding Remarks Finally, we have reached the conclusion of this section. In the Flix-Finder, we were able to integrate data updating functionality. As a result, our Flix-Finder will always be available to its users with fresh data! I hope you liked the three-part series. This contains the whole collection's source code. Github repository You can for more such amazing articles. See you in the next article… Till then, Happy Learning! 🙂 follow me on HackerNoon

Flix-Finder: Building a Movie Recommendation App With Django and Bright Data [Part 3/3]

ফ্লিক্স-ফাইন্ডার: জ্যাঙ্গো এবং উজ্জ্বল ডেটা সহ একটি মুভি সুপারিশ অ্যাপ তৈরি করা [পর্ব 3/3]

Flix-Finder: Erstellen einer Filmempfehlungs-App mit Django und Bright Data [Teil 3/3]

Flix-Finder: Creación de una aplicación de recomendación de películas con Django y Bright Data [Parte 3/3]

Flix-Finder : créer une application de recommandation de films avec Django et Bright Data [Partie 3/3]

फ़्लिक्स-फ़ाइंडर: Django और ब्राइट डेटा के साथ एक मूवी अनुशंसा ऐप बनाना [भाग 3/3]

Flix-Finder: Django と Bright Data を使用した映画おすすめアプリの構築 [パート 3/3]

Flix-Finder: Django와 Bright Data로 영화 추천 앱 만들기 [3/3부]

Flix-Finder: Construindo um aplicativo de recomendação de filme com Django e Bright Data [Parte 3/3]

Flix-Finder: Django ve Parlak Verilerle Film Önerisi Uygulaması Oluşturma [Bölüm 3/3]

Flix-Finder: Xây dựng ứng dụng đề xuất phim với dữ liệu Django và Bright [Phần 3/3]

Flix-Finder：使用 Django 和 Bright Data 构建电影推荐应用程序 [第 3/3 部分]

You are reading the second part of a three-part series. It is highly encouraged to read the first article before proceeding! here We started working on “ ” with a vision of developing a software solution to the problem of picking a movie based on numerous rating parameters. By the end of the previous part of this series, enabled us to filter films based on release year and minimum IMDB rating. But as we argued, our application will stand true to its existence only if it can filter movies across several rating parameters. Flix-Finder Flix-Finder Today, in the second part of this series, we will be adding support for filtering movies based on Rotten Tomato ratings as well! With this feature implemented, our application would be able to search movies based on two different rating parameters. Additionally, with the method outlined in this article, you (the reader) can add as many other rating parameters as you want! Let us get coding… Rotten Tomato Rating Data Our software utilizes information gathered from multiple rating agencies. For the first section, we used pre-curated IMDB rating information from . The same thing is required for Rotten Tomatoes. Bright Data A quick search indicates that Rotten Tomato does not offer a developer API for retrieving such information. Additionally, Bright Data does not offer a pre-curated dataset for Rotten Tomatoes. No! We have a route out in the form of Bright Data’s custom web scrapers! Does this imply that we are at a dead end? Bright Data’s Custom Collectors Bright Data’s collector is a tool that can be used to collect web data at large scales. It provides an integrated development environment (IDE) that enables the automation of any data-collecting procedure. This environment comes with a sizable number of pre-defined functions to make some of the typical data-collecting tasks easier. What, then, is a Collector? Before writing our own custom collector for collecting Rotten Tomatoes rating data, we should first understand some terminologies surrounding these collectors. A "stage" is a particular phase of data collecting. When users wish to gather data from several places or want to carry out numerous activities while collecting data, stages can be added. Users may more easily control and monitor the development of their data-gathering efforts by segmenting the total data collection process into several stages. For instance, a YouTube data collector may contain steps like searching, playing each video that comes up in the search results, and retrieving all of the comments. Collector Stages: In Bright Data IDE, the "interaction code" refers to the code that is executed during the data collection process to interact with the web page. It is a code written in JavaScript that runs within the context of the web page being scraped. The interaction code can be used to perform a wide range of actions, such as clicking buttons, scrolling through the page, and filling out forms. Interaction Code: It contains the logic for parsing the HTML from the interactions made in the interaction code. We use the method from the interaction code to call the parser code. Parser Code: parse() And that is all we need to know to get started with writing our first collector! Collector For Rotten Tomato Having understood the theory, let us now move on to the practical. We will be using data from Rotten Tomato's . This list contains a large number of movie ratings (~2000) across various genres. all-time lists So this is how we will structure stages for our collector: The first stage would be responsible for opening various lists from the all-time lists page. The second stage would be responsible for collecting movie names, ratings, and years of release. Stage 1: Opening Movie List According to the description above, this stage should be able to navigate to the all-time lists page, capture the URLs for all the lists on the page, and pass them one by one on to the next stage. This is how the interaction code for this stage will look like. let url = new URL('https://editorial.rottentomatoes.com/all-time-lists/') // (1)
navigate(url) // (2) 
let hrefArray1 = parse().hrefArray; // (3) 
hrefArray1.forEach(href => next_stage({ // (4)
 url: href
}))

let url2 = new URL('https://editorial.rottentomatoes.com/all-time-lists/?wpv_view_count=1773-CATTR9a61a7f98d435e1c32de073e05574776&wpv_paged=2');
navigate(url2);
let hrefArray2 = parse().hrefArray;
hrefArray2.forEach(href => next_stage({
 url: href
})) Let us understand this code, line-by-line. We create a new URL object. This class is provided by default in the IDE by Bright Data. We provide the URL for the all-time list as a parameter. We navigate to this URL using another function provided. This is essentially equivalent to opening a web page. We next call the parser code that will parse the data and return a list of URLs. These URLs correspond to different lists on the page. We then call the next stage for each of the lists. Yet again, IDE provides us with the next_stage method to send inputs to the next stage. We repeat these same steps for another URL that corresponds to the second page of the all-time list. Next, let us look at the parser code for this stage. var hrefArray = []; //create an empty array to store href values

$("div.col-sm-8.newsItem.col-full-xs a.unstyled.articleLink").each(function() {

 var href = $(this).attr("href"); //get the href attribute value of current 'a' tag

 hrefArray.push(href); //push href value to the array

});



return {hrefArray}; The parser logic is pretty simple. We have written a selector for selecting all the URLs from the different lists available on the page. Stage 2: Collecting Movie Data The second stage receives a URL as input. This URL refers to the web page containing a list of movies. The responsibility of this stage should be to navigate to the provided URL and collect all the movie data available on that page. Let us look at the interaction code for it. navigate(input.url);
let {movieData} = parse();
movieData.forEach(movie => collect(movie)) As described, we navigate to the provided URL and call the parser. The parser returns a list of movie data. We call the method to “collect“ that data! collect This is what the parser code looks like. let movieData = []
$('div[id^="row-index-"]').each(function() {
 let posterSrc = $(this).find("div > a.article_movie_poster > div > img").attr("src");
 let movieName = $(this).find('div.article_movie_title div > h2 > a').text();
 let movieYear = $(this).find('span.subtle.start-year').text().slice(1, -1);
 let movieRating = $(this).find('span.tMeterScore').text();
 movieData.push({posterSrc, movieName, movieYear, movieRating})
});

return {movieData} A quick look at the parser code reveals that we are selecting four properties for each of the movies on the page. We then create a list of all such movies and return that from the parser. Great! We just finished writing our custom collector! To test that everything is working, click on the preview button (as highlighted in the image below). Once the preview is successful, you can click on the Save button on the top-right. Our collector is now saved and ready to use. Running our Collector Select the MyScrapers tab from the Datasets & WebScraper IDE menu. Your recently created collector will be visible here. Click on the three dots towards the left of your collector and select initiate manually. Click ‘Start’ on the ‘Initiate manually’ menu. This will start the collector. Wait for the process to finish. Once the run is finished, we can download the JSON file under the Actions. Check that this file contains roughly 2000 movies! Rename the downloaded file to and place it under the static folder of Django app. This folder should already contain the IMDB data file and the CSS file for our application. rtm.json recommender With all this, the data for our application is ready, and we can begin updating our application! Adding an extra field to our Django Form Continuing from the form we created in the last part, we will be adding three new fields to our form. Following will be the fields - (1) : Sets a threshold for the minimum Rotten Tomato Rating Minimum Rotten Tomato Rating (2) : Checkbox to indicate if we want to include those movies for which Rotten Tomato ratings exist and meet the threshold, but IMDB ratings are not present. Include Empty IMDB Ratings (3) : Checkbox to indicate if we want to include those movies for which IMDB ratings exist and meet the threshold, but Rotten Tomato ratings are not present. Include Empty Rotten Tomato Ratings Here is what the updated class looks like. from django import forms

class MovieSearchForm(forms.Form):
 imdb_rating = forms.DecimalField(min_value=0.0, max_value=9.9, label='Minimum IMDB Rating')
 rotten_tomato_rating = forms.DecimalField(min_value=0.0, max_value=9.9, label='Minimum Rotten Tomato Rating')
 include_empty_rotten_tomato_ratings = forms.BooleanField()
 include_empty_imdb_ratings = forms.BooleanField()
 release_year = forms.IntegerField(required=True) With this change, you should be able to see the updated form on the webpage! Updating the View With both the data and the form in place, what remains is to update our view so that recommendations can be made considering the Rotten Tomato ratings as well. Let us first write a method that can read the static JSON file and create a list of Python objects representing Rotten Tomato Rating data. # recommender/views.py

import json

ROTTEN_TOMATO_DATA_FILE_PATH = './recommender/static/rtm.json'

class RottenTomatoMovieData:
 def __init__(self, movie_title, rating, release_year) -> None:
 self.movie_title = movie_title
 try: 
 self.rating = float(rating.strip('%')) / 10
 except ValueError:
 self.rating = 0
 try:
 self.release_year = int(release_year)
 except ValueError:
 self.release_year = None

def _getRottenTomatoMovieDataList():
 rotten_tomato_movie_list = []
 data_file = open(ROTTEN_TOMATO_DATA_FILE_PATH, encoding="utf8")
 json_data = json.load(data_file)
 for movie_json_data in json_data:
 if 'movieName' not in movie_json_data:
 continue
 rotten_tomato_movie_data = RottenTomatoMovieData(movie_json_data['movieName'], movie_json_data['movieRating'], movie_json_data['movieYear'])
 rotten_tomato_movie_list.append(rotten_tomato_movie_data)
 data_file.close()
 return rotten_tomato_movie_list We created a new class to represent the rating data obtained from Rotten Tomato. The parsing code is pretty simple, we read the JSON file and convert each JSON object one-by-one to . RottenTomatoMovieData RottenTomatoMovieData The next thing we need to do is filter this list based on the values that the user entered in the form. Let's create another function that will accept the form data and the list of Rotten Tomatoes movie ratings. The filtered list would then be returned by this function. # recommender/views.py

def _filter_rotten_tomato_movie_list(rotten_tomato_movie_list, movie_search_form: MovieSearchForm):
 filtered_list = []
 for movie in rotten_tomato_movie_list:
 if movie_search_form.cleaned_data['rotten_tomato_rating'] and movie.rating < movie_search_form.cleaned_data['rotten_tomato_rating']:
 continue
 if movie.release_year and movie.release_year != movie_search_form.cleaned_data['release_year']:
 continue
 filtered_list.append(movie)
 return filtered_list Again, the filtering logic is straightforward. We iterate over the list of movies and filter out any movie whose rating does not match the threshold or the release year is different from what is requested. Merging Movie Data We have a list of movies from IMDB rating data after part 1. This list was used to present the results on our website. Using the Rotten Tomatoes ratings, we have now produced another list of movies. These two lists must be combined in some way to produce a single list that can be used to display results on the result page. # recommender/views.py

class MovieData:
 def __init__(self, movie_title, imdb_rating, rtm_rating, release_year):
 self.movie_title = movie_title
 self.imdb_rating = imdb_rating
 self.rtm_rating = rtm_rating
 self.release_year = release_year
 def __str__(self) -> str:
 return f"{self.movie_title} {self.imdb_rating} {self.rtm_rating} {self.release_year}"

def _merge_movie_lists(imdb_movie_list, rtm_movie_list, form: MovieSearchForm):
 merged_movie_list = []
 movie_name_movie_year_to_movie_data = defaultdict(lambda: {})
 for imdb_movie in imdb_movie_list:
 movie_name = imdb_movie.movie_title
 imdb_rating = imdb_movie.rating
 rtm_rating = None
 release_year = imdb_movie.release_date.year
 movie_name_movie_year_to_movie_data[movie_name.lower()][release_year] = MovieData(movie_name, imdb_rating, rtm_rating, release_year)
 for rtm_movie in rtm_movie_list:
 movie_name = rtm_movie.movie_title
 rtm_rating = rtm_movie.rating
 imdb_rating = None
 release_year = rtm_movie.release_year
 existing_object = movie_name_movie_year_to_movie_data[movie_name.lower()].get(release_year, None)
 if not existing_object:
 movie_name_movie_year_to_movie_data[movie_name.lower()][release_year] = MovieData(movie_name, imdb_rating, rtm_rating, release_year)
 else:
 existing_object.rtm_rating = rtm_rating
 movie_name_movie_year_to_movie_data[movie_name.lower()][release_year] = existing_object
 for movie_year_to_movie_data in movie_name_movie_year_to_movie_data.values():
 for movie_data in movie_year_to_movie_data.values():
 if not form.cleaned_data['include_empty_rotten_tomato_ratings'] and movie_data.rtm_rating is None:
 continue
 if not form.cleaned_data['include_empty_imdb_ratings'] and movie_data.imdb_rating is None:
 continue
 merged_movie_list.append(movie_data)
 return merged_movie_list This code might look overwhelming at first sight! But let us break this down to better understand this. (1) We first create a map from the movie name and movie release year to actual movie data. (2) We fill this map for IMDB movie data first. (3) Next, we try to fill this map for Rotten Tomato movie data. (4) Once the map is populated with data from both lists, we filter the entries in this map based on the values provided in the form by the user. (5) We finally return the merged list. Final Result Okay… with all that work done, What do we have? We can try searching for movies based on two different ratings, and our app WORKS! With all the methods described in this article, you can add many more such ratings. It is all up to the imagination of readers! Code For This Series All code created as a part of this series will be available on GitHub repository. The final result of this part is contained in branch named . this this Part2 It is always a good idea to cross-check your code with the one present on the repo to check if something is wrong. Concluding Remarks With this, we come to the end of the second part of this three-part series. In the next part, we will automate the process of data delivery so that our application stays up to date with all the new data. See you next time… Till then, Happy Learning! 🙂

Flix-Finder: Building a Movie Recommendation App With Django and Bright Data [Part 2/3]

Have you ever struggled to decide which TV show or movie to watch next? Making a decision about your next binge-watch is never easy because there are so many options accessible on different streaming providers. When deciding on our next movie, we primarily rely on reviews and ratings from several rating agencies (like IMDB). However, choosing which rating to believe in might be difficult given the abundance of these institutions. Having said that, it is frequently safer to view a movie that has received good marks from a number of these organizations. But isn’t finding such a movie difficult? Yes, it is! In this three-blog series, we will be creating a software solution to solve this exact problem! This Django application, which we will be calling Flix-Finder, would allow its user to filter movies based on ratings from several rating agencies. We will use BrightData’s services to grab the required data for our application. So without further ado, let's start building Flix-Finder. Setting Up the Application Before starting with this section, do ensure that Django is installed in your system! The following command should work if Django is installed in your system. python3 -m django --version Use pip to install Django if it is not installed in your system! python3 -m pip install Django Let us begin by setting up our Django project. Create a new project using the following command. rishabh@my-ubuntu:~/projects$ python3 -m django startproject flix_finder Change the directory to the newly created project directory . Open your favorite code editor here. You should see the following files in this directory automatically created by Django. flix_finder rishabh@my-ubuntu:~/projects/flix_finder$ tree .
.
├── flix_finder
│   ├── asgi.py
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
└── manage.py

1 directory, 6 files To know more about these files, refer to this provided by Django. explanation Try running the Django server and accessing to ensure that the setup was completed properly! localhost:8000 rishabh@my-ubuntu:~/projects/flix_finder$ python3 manage.py runserver Creating a New App & Our First View A Django app (or Django application) refers to a component of a larger Django project that encapsulates an individual feature or functionality. Django apps are reusable and independent components. They consist of models, views, templates, tests, and other files. All of the recommender logic for our project would reside in an application called . To create this app, run the following command. recommender rishabh@my-ubuntu:~/projects/flix_finder$ python3 manage.py startapp recommender The above command will create a new folder named . All of our application’s logic would go inside this folder. recommender There is one more step remaining before we can say that our application is created successfully. Go ahead and add the name of the newly created app in the list present under . INSTALLED_APPS flix_finder/settings.py # flix_finder/settings.py

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'recommender'
] With this, we have successfully created our app. recommender Now, let us create our first view. In Django, views are Python functions that receive HTTP requests and return HTTP responses. Views play an important role in presenting data to users based on some business logic. Create a new method under . This method would serve requests coming through . For now, this endpoint simply returns a hello message. recommender/views.py localhost:8000/ from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello From Recommender!") We have created the view method for handling the request, but we haven’t specified (in our project) which endpoints correspond to this view. We do this in the files. urls.py Create a new file inside the recommender app, and name it . Here, we specify this correspondence between the URL and the view method. urls.py from django.urls import path

from . import views

urlpatterns = [
    path("", views.index, name = "index")
] We now need to make Django aware of this new URL’s file. Update the file to the following. flix_finder/urls.py from django.urls import path, include

urlpatterns = [
    path('', include("recommender.urls")),
    path('admin/', admin.site.urls),
] Start your server, if not already, and open on your browser. You should see the hello message. localhost:8000 Congratulations, you just completed creating your first view! Let Us Use Templates… Let us agree… sending a string as a response is not the coolest of things. We should instead send a proper HTML response. And for this exact purpose, we will be using Django Templates. Create a directory under the director. The name of this newly created folder is special as Django by-default searches for HTML files in the directories named . templates recommender templates Create a new file named inside the newly created directory. Inside this file, we will write the HTML that we want to render when users visit . index.html localhost:8000 Since writing HTML is not the main focus here, I will skip explaining that part and you can simply copy the HTML from . here Here are some of the things that you need to know about this HTML file (or Django template) - This HTML displays four things on the webpage - Navigation bar, Carousel, Movie Search Form, and Movie Search Results. (1) It uses the Django template language to allow the use of Python variables directly inside the HTML. (2) It expects variable in the context (data passed to template) and will use it to display the carousel. (3) featured_movies It expects the variable to be filled with a Django Form object and will use it to display the Movie Search Form. (4) form It expects the variable and will use it to display the results of search. (5) filtered_movie_list It also expects variable that indicates if the search resulted in an empty response. (6) no_result_error We will also need to add some CSS for styling. Create a directory under the directory. This folder will hold all the static files. static recommender Create a new file named , and paste the CSS from . style.css this URL With this, we have all the required HTML and CSS in place. Next, we need to create the Movie Search form. Creating the Movie Search Form Since our template requires us to provide a form object, we will need to create one. Not sure what are Django Forms? Read ChatGPT explain this - Django forms are a module in the Django framework used to create and work with HTML forms. They provide several advantages over using plain HTML forms: Automatic rendering of HTML. You define a Django form class, and Django renders it as an HTML form. Validation of data. Django forms validate the submitted data and clean it before saving. Binding data to forms. You can bind existing data to a form for displaying to the user for edit. Create a file named inside the recommender’s app directory. Add the following code to it. forms.py from django import forms

class MovieSearchForm(forms.Form):
    imdb_rating = forms.DecimalField(min_value=0.1, max_value=9.9, required=True, label='Minimum IMDB Rating')
    release_year = forms.IntegerField(required=True) What we just did is created a form class for our Movie Search Form. This form will contain two fields as described in the class. If you want to know more about forms, check out . this Updating the Index View With all the things required for templates set, now we need to update our view method to use this newly created template. from .forms import MovieSearchForm

# We use a hard-code list here, we will make it dynamic in subsequent parts
featured_movie_list = [
    {
        "img_src": "https://images.thedirect.com/media/article_full/amazing-spider-man-3-andrew-garfield-movie-mcu.jpg",
        "img_alt": "...",
        "active": True
    },
    {
        "img_src": "https://static1.colliderimages.com/wordpress/wp-content/uploads/2022/11/avatar-the-way-of-water-poster.jpg",
        "img_alt": "...",
        "active": False
    }
]


def index(request):
  context = {
    "featured_movies": featured_movie_list,
    "form": MovieSearchForm()
  }
  return render(request, 'index.html', context) Start your server, and head to . You should see the following page. localhost:8000 IMDB Data From BrightData With all of the views, URLs, templates, and forms in place, we can now consider the data that our application will operate on. Our program would require data from multiple rating agencies (IMDB, to begin with). Unfortunately, there is no well-defined API via which we may retrieve this data. One method for obtaining this information is to create our own scrapers using Python Selenium. However, this would require more work, and not everyone is comfortable with such technologies. A better solution is to use third-party services like BrightData. BrightData makes it easy to obtain all of this information. For this 1st part, we will be using a pre-curated IMDB dataset from BrightData. This is one of the many datasets that are available on BrightData. This is how we can get it. Signup for an account on BrightData. (1) Either go to from the browser or open menu from the left. (2) brightdata.com/cp/datasets Datasets & WebScraper IDE Go to Dataset Marketplace, and search for . (3) IMDB media dataset Click on View Dataset, and download the sample dataset in CSV format. (4) Once downloaded, rename the file to , and place it inside the folder. This sample CSV contains 28 IMDB attributes for 1000+ movies. Our application will work on this IMDB dataset for this part. IMDB.csv recommender/static Updating the Index View to Receive a Form Submission Let us update the view method to process search requests. All the search requests from the form arrive as POST requests. After confirming that the form is properly filled, we will list out all the movies from the downloaded CSV file and filter out those that do not match the filter criteria. Here is how the updated code looks. def index(request):
    if request.method == 'POST':
        form = MovieSearchForm(request.POST)
        if form.is_valid():
            movie_list = _getIMDBMovieDataList()
            filtered_movie_list = _filter_imdb_movie_list(movie_list, form)
            context = {
                "featured_movies": featured_movie_list,
                "form": MovieSearchForm(),
                "filtered_movie_list": filtered_movie_list,
                "no_result_error": len(filtered_movie_list)==0
            }
            return render(request, 'index.html', context)
    context = {
        "featured_movies": featured_movie_list,
        "form": MovieSearchForm()
    }
    return render(request, 'index.html', context) The implementation of the methods and is nothing specific to Django and is just pure Python. I thus skip talking about that here. The entire file can be found . _getIMDBMovieDataList _filter_imdb_movie_list views.py here Final Product Congratulations on reaching this point. If you have followed all steps properly, you should be able to run the server and perform searches through UI. Here is how the search results would look. Code For This Series All code created as a part of this series will be available on GitHub repository. The final result of this part is contained in branch named . this this Part1 It is always a good idea to cross-check your code with the one present on the repo to check if something is wrong. Concluding Remarks With this, we come to the end of the first part of this three-part series. In the next part, we will use BrightData’s WebScraper IDE to collect data from other rating agencies. We will then update our form, thus allowing users to perform more complex searches. We will also be making the Carousel component dynamic! See you next time… Till then, Happy Learning! 🙂

Flix-Finder: Building a Movie Recommendation App With Django and BrightData [Part 1/3]

ফ্লিক্স-ফাইন্ডার: জ্যাঙ্গো এবং ব্রাইটডেটা দিয়ে একটি মুভি সুপারিশ অ্যাপ তৈরি করা [পর্ব 1/3]

Flix-Finder: Erstellen einer Filmempfehlungs-App mit Django und BrightData [Teil 1/3]

Flix-Finder: Creación de una aplicación de recomendación de películas con Django y BrightData [Parte 1/3]

Flix-Finder : créer une application de recommandation de films avec Django et BrightData [Partie 1/3]

फ़्लिक्स-फाइंडर: Django और BrightData [भाग 1/3] के साथ मूवी अनुशंसा ऐप बनाना

Flix-Finder: Django と BrightData を使用した映画おすすめアプリの構築 [パート 1/3]

Flix-Finder: Django와 BrightData를 사용하여 영화 추천 앱 구축 [1/3부]

Flix-Finder: Construindo um aplicativo de recomendação de filmes com Django e BrightData [Parte 1/3]

Flix-Finder: Django ve BrightData ile Film Önerisi Uygulaması Oluşturma [Bölüm 1/3]

Flix-Finder: Xây dựng ứng dụng đề xuất phim với Django và BrightData [Phần 1/3]

Flix-Finder：使用 Django 和 BrightData 构建电影推荐应用程序 [第 1/3 部分]

Welcome to another edition of Hack marketing diaries by team HackerNoon! This time, we wanna share our learnings and observations on surveillance capitalism and why we didn't make it our business model. There’s a reason we hate those invasive Ads on Facebook. Who would like their personal and critical information to be used to see those creepily relevant Ads? According to - Meta was also involved in a major about its access to the medical data of patients. The company’s pixel tracking tool was collecting their sensitive information from hospitals including the details about their health, appointments, and medical prescriptions. Pure evil. What's even more annoying? Abrupt Ads stoping you from consuming the content. Classic example - Ads on YouTube. the Markup “Major tax filing services such as H&R Block, TaxAct, and TaxSlayer have been quietly transmitting sensitive financial information to Facebook when Americans file their taxes online.” controversy last year Here’s how HackerNoon saw these issues when we used to become not only a publishing company but also a software company: equity crowdfunding “We don't want to advertise the shoes you've considered buying around the web, we do want to surface sponsors that are relevant to the subject matters being read, such as blockchain sponsors on blockchain content. We believe sponsorship is not evil, if done right. What's evil is surveillance. Our sponsors will be by subject matter. Our promotions are like billboards - relevant to the content and not the individual reader. Our sponsorship placements are minimalistic & non-intrusive. No sidebars, no popups, no video ads. We care about reading quality and will never take that experience away from the reader.” Presenting you HackerNoon’s Targeted Ads by content relevancy - where you target the audience interested in content relevant to your company, without invading their privacy or shoving annoying pop-ups down their throats. Our targeted marketing options allow you to avoid the noise and reach the right audience. It’s a win-win for all. Talking about wins - here's an easter egg for your company - use the code 'HackerFriend' during the meeting and avail 10% off on Targeted Ads! Book a call with us to see the demo Here’s how our targeted Ads by content relevancy work: HackerNoon has curated to date 50,000+ technology tags These tags are grouped into the relevant like , , , , , , and more! parent categories AI Web3 Programming Startups Cybersecurity Finance Every story organically gets at least eight relevant tech tags and a parent category Sponsors buy with all the tags and stories. multimodal placements on relevant categories These Ad placements include Banners, Logos, Newsletter ADs, and Audio ADs - what we call truly AIO (Activities, Interests, and Opinions) Optimized for: Brand Recall and Clickability (Get 3x clicks for the same impressions compared to elsewhere) Get quality leads at unbeatable prices, with CPM ~ $7 and CPC ~ $5. Find pricing and details in the attached deck. to learn how AWS, Bosch, Brave, DataStax, Bright Data, Twilio, SonarSource, Algolia, Algorand, Postman, IONOS, and thousands of other tech companies partner with HackerNoon to target their audiences. Book a meeting with us Until next time!

How to Advertise to Your Target Audience without Creeping Them Out

কীভাবে আপনার টার্গেট শ্রোতাদের কাছে বিজ্ঞাপন দেওয়া যায় তাদের আউট না করে

So werben Sie bei Ihrer Zielgruppe, ohne sie zu verschrecken

Cómo anunciarse ante su público objetivo sin asustarlos

Comment faire de la publicité auprès de votre public cible sans l'effrayer

अपने लक्षित दर्शकों को बिना परेशान किए उनके लिए विज्ञापन कैसे करें

ターゲット層を怖がらせずに広告を出す方法

타겟 고객을 소외시키지 않고 광고하는 방법

Como anunciar para seu público-alvo sem assustá-lo

Как рекламировать свою целевую аудиторию, не отпугивая ее

Hedef Kitlenizi Korkutmadan Nasıl Reklam Verebilirsiniz?

Cách quảng cáo tới đối tượng mục tiêu của bạn mà không khiến họ sợ hãi

如何向目标受众投放广告而不让他们感到害怕

Surveillance capitalism… How about we not make that our business model? Here’s how HackerNoon saw it when we used to become but also a : equity crowdfunding not only a publishing company software company “We don't want to advertise the shoes you've considered buying around the web, we do want to surface sponsors that are relevant to the subject matters being read, such as blockchain sponsors on blockchain content. We believe sponsorship is not evil, if done right. What's evil is surveillance. Our sponsors will be either site-wide or by subject matter. We think there is a major difference between a "sponsor" and an "advertiser": Our promotions are like billboards - relevant to the content and not the individual reader. Without sponsors, we can't make the site free for readers forever. Our sponsorship placements are minimalistic & non-intrusive. No sidebars, no popups, no video ads. We care about reading quality and will never take that experience away from the reader. One of the main reasons Jonah Peretti left Huffington Post and started Buzzfeed was because the ad system was not baked into the tech. By increasing the ad duration and having no sales middlemen, Hacker Noon has been able to offer customers better rates and better relevancy, and we'll have healthy margins with longer-term customers. We want to own the relationship with the brands and have quality brands that validate the content. We have been extremely selective in choosing which brands to be HackerNoon sponsors and will continue to do so going forward.” So, how does ad placement by content relevancy work at HackerNoon now? HackerNoon has curated 50,000+ technology tags to date These tags are grouped into the like , , , , , , and more! relevant parent categories AI Web3 Programming Startups Cybersecurity Finance Every story organically gets eight relevant and a parent category tech tags Sponsors buy multimodal placements on relevant categories with all the tags and stories. These Ad placements include Banners, Logos, Newsletter ADs, and Audio ADs  - truly AIO (Activities, Interests, and Opinions) Optimized For: ( ) Brand Recall and Clickability Get 3x clicks for the same impressions compared to elsewhere to learn how AWS, Brave, DataStax, Bright Data, Twilio, SonarSource, Algolia, Algorand, Postman, IONOS, and thousands of other tech companies partner with HackerNoon to target their audiences. Book a meeting with us

Targeted Ads: Ad Placements by Content Relevancy

লক্ষ্যযুক্ত বিজ্ঞাপন: বিষয়বস্তুর প্রাসঙ্গিকতা অনুসারে বিজ্ঞাপনের স্থান

Gezielte Anzeigen: Anzeigenplatzierungen nach Inhaltsrelevanz

Anuncios dirigidos: ubicaciones de anuncios por relevancia del contenido

Annonces ciblées : emplacements d'annonces par pertinence du contenu

लक्षित विज्ञापन: सामग्री प्रासंगिकता के आधार पर विज्ञापन प्लेसमेंट

ターゲットを絞った広告: コンテンツの関連性による広告の配置

타겟 광고: 콘텐츠 관련성에 따른 광고 배치

Anúncios direcionados: canais de anúncios por relevância de conteúdo

Таргетированная реклама: места размещения объявлений по релевантности контента

Hedefli Reklamlar: İçerik Alaka Düzeyine Göre Reklam Yerleşimleri

Quảng cáo được nhắm mục tiêu: Vị trí đặt quảng cáo theo mức độ liên quan của nội dung

定向广告：按内容相关性划分的广告展示位置

Your web scraper has just been blocked by Cloudflare again! You've looked online for solutions, but nothing seems to do the trick. Is there a secret sauce for Cloudflare bypass? You bet there is. You just haven't stumbled upon the right recipe! Embark on a guided tour through the maze of Cloudflare anti-bot technology. We're here to demystify Cloudflare's inner workings, unveil some game-changing tricks, and ensure you never hit a dead end again! What Is Cloudflare? , the cyber fortress of the Internet, offers top-notch services to boost the security and performance of websites and web applications. But you probably knew that already! Cloudflare Now, what's the secret sauce that makes Cloudflare so popular? Drumroll, please—it’s anti-bot solutions! Ever come across that page? Well, you’re the only one! That’s a result of the , a high-tech shield against the mischief of malicious bots. Cloudflare Bot Management service Here are the technologies that power that cybersecurity platform: Behavioral analysis: Picture it as Sherlock Holmes scrutinizing user behavior on the Web to decipher whether users are humans or not. Machine learning magic: Think of it as the Dumbledore's Army of bot detection, learning from a colossal dataset with billions of records to give every request a reliable bot score. Fingerprinting: Use millions of internet properties to precisely identify bot requests without compromising user privacy. The result? Bots, begone! From stopping credential stuffing to thwarting content scrapers, spammers, inventory hoarders, credit card stuffers, and even putting up a solid defense against application DDoS attacks—Cloudflare's got it covered. Cloudflare’s Bot Management stands tall as the Batman of bot management, vigilantly safeguarding websites to thwart any hint of malicious bot behavior. 🦸‍♂️ But every superhero has its kryptonite! Dive into the next section to unravel more. Tips to Bypass Cloudflare Before digging into this chapter, let's tackle a burning question. Is it even possible to bypass Cloudflare? Absolutely! No online solutions can or should restrict users from accessing public sites as they wish, even through automated software. Thus, the million-dollar question is: how to outsmart Cloudflare? 🤔 Dive into the tips below to master the art of eluding Cloudflare! Use a Reliable IP Cloudflare gives IPs a score based on their reputation. If your IP has been caught red-handed performing actions considered malicious by Cloudflare, the chances of getting blocked become high. The solution? Having a large pool of IPs to distribute your requests on. In that scenario, even if one IP gets blocked, you have many others available in your arsenal. That’s where IP rotation via proxy comes into play. Find out more on . how to rotate an IP address Leverage Browser Automation Only browsers can overcome the challenges set by Cloudflare’s Bot Management. When you navigate a page shielded by that solution, your browser uses JavaScript to tackle those challenges without you even realizing it. Requests made by an automated script through a traditional HTTP client will then hit an immediate roadblock. Why? Because HTTP clients can’t execute JavaScript! That’s something only a browser can do. Thus, your Cloudflare-ready automated script should rely on an approach for programmatically executing actions on a web page in a browser, such as . controlling a headless browser via Playwright Reverse Engineering the Cloudflare Challenges This is where the real challenge kicks in—how to outsmart the anti-bot defenses intricately embedded into a web page by Cloudflare? The answer: dive deep into their workings, unravel the puzzle through reverse engineering, and conquer. That's easier said than done 😅 Step one involves finding the request linked to the retrieval of the JavaScript challenge. This will involve encrypted code that you must somehow decipher: Suppose you managed to do that. Now, all that remains is to figure out the original code's intentions and create a strategy to fool it into thinking that the Cloudflare challenge has been overcome. On paper, this sounds like a valid approach, but in the real world, it may be a one-way ticket to a colossal headache. Don’t worry, though. Overcoming Cloudflare is possible—you just need the right tools! My Solution Works… Until It Doesn't Imagine you're an IT genius who manages to crack the code to get past Cloudflare's bot protection technologies. Bear in mind that Cloudflare is an ever-evolving beast, so no guarantees your solution will work tomorrow. To reach the goal, you've got to embrace a different approach! A real-world, ideal, intelligent solution to overcome Cloudflare would be a controllable browser that: Can overcome advanced anti-bot measures for you. Is always up-to-date because it runs in the cloud. Automatically rotates IPs and handles request attempts. Avoids fingerprinting to improve your privacy. Can resolve CAPTCHAs on its own. Allows cookie management and header customization. Ensures amazing scalability and performance by executing in the cloud. It can be controlled by any browser automation library, such as Selenium, Playwright, or Puppeteer. This isn't a mere dream, but a palpable reality. The browser you're imagining is real, developed by Bright Data, and goes by the name of ! Scraping Browser Final Thoughts Cloudflare reigns supreme in safeguarding websites against malicious bots. Here, we've delved into the technologies it uses for detecting and stopping automated software, offering you some handy tips to avoid them. But let's be real, that’s a grueling, trial-and-error, demoralizing task. Need to bypass Cloudflare once and for all? Embrace Scraping Browser from and join our mission to transform the Internet into a public space accessible to everyone, everywhere, even via automated scripts! Bright Data Until next time, keep exploring the Internet with freedom!

BRIGHT DATA

brightdata.com

201-500 employees

Since 2014

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BRIGHT DATA

brightdata.com

201-500 employees

Since 2014

COMPANY RANKING

BRIGHT DATA

EVERGREEN INDEX #13

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Representing this company? Click here to claim and customize this page!

bright data WIKI