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How to Perform Web Scraping with PHP


In this tutorial, you will learn what web scraping is and how you can do it using PHP. We will extract the top 250 highest-rated IMDB movies using PHP. By the end of this article, you will have sound knowledge to perform web scraping with PHP and understand the limitation of large-scale data acquisition and what your options are when you have such requirements.

What is web scraping?

We surf the web every day, looking for information we need for an assignment or simply to validate certain hunches. Sometimes, you may need to copy some of that data or content from a website and save it in a folder for use later. If you’ve done that, congrats, you have essentially done web scraping. Welcome to the club!

But, when you need massive amounts of data, your typical copy-paste method will prove to be tedious. Data as a commodity only makes sense when you extract it at scale within a context.

Web scraping, or data extraction then, is the process of collecting data from multiple sources on the web and storing it in a legible format.

Data is something of a currency in this day and age, and companies are increasingly looking to be data-driven.

But without a proper framework and data management protocols overarching the entire data lifecycle, the currency of the twenty-first century is as good as an expired coupon. We’ve always maintained that bad data is no better than no data. Read about the five primary characteristics of high-quality data here:

The main scope of this article is to introduce you to the world of data extraction using one of the most popular server-side scripting languages for websites -PHP. 

We will use a simple PHP script to scrape IMDB’s top 250 movies and present it in a readable CSV file. Considering PHP is one of the most dreaded programming languages, you might want to take a close look at this one. The difficulty level of web scraping with PHP is just about perspective.

PHP fundamentals for web scraping

The technology that establishes a connection between your web browser and the many websites throughout the internet is complex and convoluted.

Roughly 40% of the web is fueled by PHP, which is reputed to be historically messy, in terms of both logical and syntactical grounds.

PHP is an object-oriented programming language. It supports all the important properties of Object Oriented Programming like abstraction and inheritance which is best suited for long-term scraping purposes.

Although data extraction is relatively easier with other programming languages, most websites today have more than a hint of PHP, making it convenient to write a crawler faster and integrate them with websites.

Before we go any further, let’s briefly outline the content of this article: 

  • Prerequisites
  • Definitions
  • Setup
  • Creating the scraper
  • Creating the CSV
  • Final words

Prerequisites: For data extraction using PHP

Firstly we will need to define what we will be doing and what we will be using for this scraping tutorial. Our general workflow will consist of setting up a project directory and installing necessary tools required for data extraction.

Most of these are platform agnostic and can be performed in any operating system of your choice.

Then we will go through each step of writing the scraper in PHP using the mentioned libraries and explaining what each line does.

Finally, we will go through the limitations of crawling and what to do in case of large-scale crawling.

The article will address mistakes one might unknowingly make. We will also suggest a more appropriate solution.

Definitions to get you started with PHP web scraping

Before we get into the thick of the action, let’s cover some basic terms you will come across when reading this article. All the technical terms will be defined here for ease of demonstration.

1. Package manager

A package manager helps you install essential packages through a centralized distribution storage. It is basically a software repository that provides a standard format for managing dependencies of PHP software and libraries.

Though not limited to managing PHP libraries, package managers can also manage all the software installed in our computers like an app store but more code specific.

Some examples of package managers are: Composer (for PHP), npm (for JavaScript), apt (for Ubuntu derivative linux), brew (for MacOS), winget (for Windows), etc.

2. Developer console

It’s a part of the web browser that contains various tools for web developers. It is also one of the most used areas of the browser if we are to start scraping data from websites.

You can use the console to determine the tasks a web browser is performing when interacting with a website under observation. Although there are many sections to pick from, we will be using only Elements, Network, and Applications sections for the purpose of this article.

3. HTML tags

Tags are specific instructions written in plain text enclosed by triangular brackets (greater than/less than sign).


<html> … </html>

They are used to give instructions to the web browser on how to present a web page in a user-friendly manner.

4. Document Object Model (DOM)

The DOM consists of the logical structure of documents and the way they are accessed and manipulated.

Simply put, DOM are models generated from an HTML response, which can be referenced through simple queries without resorting to complex processing.

A good example would be an interactive book where each complex word is linked to its meaning as soon as one clicks on the word.

5. Guzzle/guzzlehttp

It is an external package used by our scraper to send requests to and from the web server, similar to a web browser. This mechanism is often referred to as the HTTP handshake where our code sends a request (termed GET request) to the IMDB servers.

In response, the server sends us a response body, which consists of a set of instructions with the proper response body, cookies (sometimes), and other commands that run inside the web browser.

Since our code will be running in a sequential form (one process at a time), we will not handle other instructions provided by the IMDB servers. We will focus only on the response text. You can find the documentation for this package here.

6. Paquette/php-html-parser

Like guzzle, this is also an external package used to convert raw response from the web page received by guzzle client into a proper DOM.

 By converting into a DOM, we can easily reference the parts of the document received and access individual parts of the document which we are trying to scrape. The source code and documentation for this package can be found here.

7. Base URL

Base URLs are the URLs of websites that point to the root of the web server.

You can get a better understanding of the base URL by reviewing how a folder structure works in the computer system.

Take a folder called Documents in the computer. Now this is what the web server exposes to the internet. It can be accessed by any user requesting a response from the web page.

We can open any new folder in the documents folder. Navigating to the new folder is simply a matter of traversing the Documents/newfolder/path.

Similar to how web pages are maintained based on hierarchy, the base urls are the root of the entire web page’s web document, and any new pages are simply “folders” inside that base URL folder.

8. Headers

Headers are instructions for the web servers to follow rather than our client system. They provide a simple collection of predefined definitions, which allow web servers to accurately decode client responses.

A basic example would be a download windows page, say in

With the user-agent header, the web server can easily deduce that the request sent to their server comes from a windows PC. Hence, it needs to send information that is relevant to the platform. The same logic applies for language differences between web pages.

9. CSS selectors

CSS Selectors are simply a collection of text syntax that can pinpoint a document in a DOM without using much processing resources.

It is similar to the table of contents section in a physical book. By looking at the table of content, the reader can skim to the sections he is interested in.

But in contrast to the table of contents, CSS selectors can accept more filters and are able to use that to reduce noise (unimportant data) from the actual data we need to search in the DOM.

They are mostly used in web designing but are mighty helpful in web scraping.

Project Setup for data extraction with PHP

After this point, the article will assume that you have a basic understanding of Object Oriented Programming and PHP. You should have skimmed through the definition presented in the above section.

It will provide you with the basic knowledge necessary to continue along with the tutorial in the following sections. We will now delve into the setup of the crawler.


Initially, we will install a package manager [1] called composer through the package manager for your systems. For Linux variants, it is simply sudo apt install composer (Ubuntu) or with any package managers in our computer. For more information about the steps to install composer, go to the link here.

Visual Studio Code (or any text editor; even notepad will do)

This is for writing the actual scraper. Visual Studio Code has multiple extensions to help you with the development of programs in different programming languages.

However, it’s not the only one that can be used to follow this tutorial. Any text editor, even basic ones, can be followed to write a scraper. 

We highly recommend IDE due to its automatic syntax highlighting and other basic features. It can be installed through the stores of individual platforms.

For Linux, installing through the package managers or Snap or Flatpaks is much easier. For Windows and MacOS installation, visit here.

Now that we have all that we need to write the scraper to extract the details of the top-rated 250 movies in IMDB, we can move on to writing the actual script.

Creating the web scraper

We want to scrape IMDB’s top 250 rated movies to date through this link for the following details:

  • Rank
  • Title
  • Director
  • Leads
  • URL
  • Image URL
  • Rating
  • Number of reviews
  • Release year

But there’s a slight hiccup. Not all the information we need is displayed on the website.

Only a handful of information is displayed on the source website

Only Rank, Title, Year, Rating and Image are directly visible.

The initial step of web scraping is to determine what the website is hiding from us.

You could take websites as walls of text sent by the web server.  When read by the web browser, the website can display different structures depending upon the instructions provided on the walls of text sent by the web server.

Every hover in each element of the website is simply an instruction to the web browser to follow the text response received from the server and act accordingly.

As a scraper our job is to manipulate this received text and extract all the information that the website wants to hide from us, unless we click on the desired option.

Step 1:

Open the developer tools [2] in the browser to check what the website has hidden from us. To open the developer console, press F12 on the keyboard or Ctrl+Shift+I (Command+Shift+I for Mac). Once you open the developer console you will be greeted with the following screen.

Developer’s console

This is basically what the current website has sent over to our system to display the website on the web browser’s canvas.

Step 2:

Now clicking on the Inspect button (Top left arrow key) will start the inspect mode for the website.

This mode is a developer mode that is used to interact with the web page as if we are trying to source the interactive element in the website to its actual instruction source on the walls of text (called response) sent over by the web server of IMDB.

Now we simply click one of the movie names and in the console, we can see what the actual text response was.

Information inside the <tr> tag

In the image above, we see there are 250 tr tags [3]. Tags in HTML are simply instructions designed for web pages to display the information in a more palatable format.

This piece of information will be useful later.

For now let’s focus on the first tr member in the response. Maximizing all the td elements, we can see more information on each movie listing than what was previously visible on the webpage.

With this information alone, we can now use the page response in our PHP code to scrape all this random information in the page into a proper tabular format, so we can generate actionable data from it.

Armed with this knowledge, we can now move on to do some real coding.

Step 3:

Create a project directory.

Let’s name the folder ‘imdb_com’ for ease of use and reference. Open the folder through the text editor and run a terminal (command prompt) in it.

After the terminal window is open, type in the following:

composer init

What this command does is invoke the composer to start a project in the folder currently active.

In our case, it is the folder we just created, i.e., imdb_com. The composer will ask us for more information. Just skim through the process and add the following packages when prompted by the composer prompt during the initial setup.

Install these packages when the composer asks for more information

Step 4:

Once in the screen shown above, type the following:


Press Enter and then paste the final package we will require:


Once the package download is complete, we will have a directory for the project as shown below:

IMDB project directory

Step 5:

Now create a new file and name it ‘imdb.php’ in the same root directory as composer.json file. We will be working on this file for the rest of the tutorial.

To start the scraper, we need to define what the PHP file is. Starting with <?php in the first line is a good start.

Import the autoload function with this keyword:

require_once "vendor/autoload.php";

This line loads the file inside the vendor folder in the root directory. It loads all the files we just installed using composer during the initial phase of running our scraper.

use GuzzleHttpClient;

use PHPHtmlParserDom;

The crawler can now start using the packages we download. Now, the question is : why use both require_once and the above script at the same time?

The answer : Require_once provides the directory which contains the necessary files to use the packages we downloaded with the composer. The ‘use’ keyword asks the program to load in the Client and Dom member of the respective classes in order for us to use these functions in our crawler.

Step 6:

Define an object for the GuzzleHttpClient and PHPHtmlParserDom.

$client = new Client([
    'base_uri' => '',
    'headers' => [
        'user-agent' => 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/ Safari/537.36',
        'accept' => 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8',
        'accept-language' => 'en-US,en;q=0.8'

This code defines a base URL [7] and headers [8] for the website we are crawling.

$dom = new Dom();

It does the same for the other library we  just defined in the initial phases of the crawler.

Step 7:

Now that all our tools are loaded in the crawler, we can get to the heart of the program.

Send a ‘GET’ request to this web page.

Note that we have already defined as a base URL for our crawler. So our actual document path to visit would be chart/top/?ref_=nv_mp_mv250, and thus to send the request, we would have to write the following:

$response = $client->request('GET', '/chart/top/?ref_=nv_mp_mv250');

Since we already have the response sent by the web server in response, we load that response into a text variable and send it to our DOM parser to generate a DOM so that referencing parts of documents will be much faster and easier.


Step 8:

We visit the web browser’s developer console again with the information we had collected before, i.e. about the 250 tr tags containing all the data we need about the movies.

All the data we need is in the tbody tag.

We see that all the movie data we need is contained in the tbody tag. The tbody tag in turn is inside the scope of the table tag.

Rather than processing the entire document since we created a DOM element using the external library, we can reference the table part of the document simply by using CSS Selectors [9].

$movies = $dom->find('table[data-caller-name="chart-top250movie"] > tbody > tr');

Now, we search the entire DOM for a table whose attribute of data-caller-name is chart-top250movie. Once that is found we go one level deeper and find all the tbody tags.

Then, we find all the tr tags by going another level deeper into the tbody tag and finally return all those tags and their members (data) and store it in the movies variable.

You can find more information about various syntaxes of CSS selectors  in this link.

Once this is done, all our movie information will be stored inside the movies variable. Iterating over each of the movies will now result in our data of 250 movies information structured in a more proper format.

You can iterate over the movies with:

foreach ($movies as $mId => $movie) {

Step 9:

Before working on the individual fields, we can introduce a new concept of overriding the DOM elements.

Since the movies variable already has all the information about all the movies we need, reusing the DOM object that has loaded the entire response from the web server is more of an optimization technique employed to reduce the memory footprint of the crawler.

Hence to reuse it, we replace the entire document with only a minuscule part of the document.

We will go into more detail after taking a small segway to another concept. We know that the tr tags contain all the information about the movies.

Copying one tr tag and expanding all the members, we get the following information about each movie (in this case, only the first one).

Everything we need is in the nested td elements.

All the information we need is present in the nested td elements. Now, we can implement the concept of reusing. Since we do not need the entire document anymore, we simply replace this information about the movie contained in the tr tag in the DOM object so we can use the same find() method to scrape the correct information we require. We can do that by using:


Step 10:

Start filling up the array with the correct key, and value index.

Since we will be replacing the DOM object at many steps throughout the loop it is wise to put all the DOM members in a separate non-replaceable variable first.

$posterColumn = $dom->find('td.posterColumn');
$titleColumn = $dom->find('td.titleColumn');
$ratingColumn = $dom->find('td.ratingColumn.imdbRating');

As we can see, rank is present in the main member of the td tag with the class name titleColumn. To extract the rank, write the following code:

$arr['Rank'] = $dom->find('td.titleColumn')->text;

Using only the above code can result in a tiny problem, as the td we just scraped contains not only the rank but also the title of the movie.

Pulling the entire td tag as text also pulls each member of the element not enclosed by tags. Therefore, we use PHP functions to split the entire text with dot (.) and only extract the first data from the array resulting from the split.

$arr['Rank'] = array_shift(explode('.', $dom->find('td.titleColumn')->text));

Now, since we do not know if there are invisible whitespaces in the text we scraped, enclosing it with trim will remove any unwanted whitespaces resulting in a numeric arr[‘Rank’];

$arr['Rank'] = trim(array_shift(explode('.', $dom->find('td.titleColumn')->text)));

To extract the attributes from a tag use the getAttributes() method:

$arr['ImageURL'] = $dom->loadStr($posterColumn)->find('img')->getAttributes()['src'];

Here, getAttributes generates an array with key value pairs where attribute names are the keys and attribute values are the values. Invoking the individual attribute names, like calling an array member using indexes will return the value we need.

Similarly, filling all the array key values will get you all the information you need about the first movie. Continuing the loop for every one of the 250 movies will result in our crawler scraping all the data we need about the 250 movies.

And whoa, our scraper is almost done!

Creating the CSV

Now that we have created the scraper, it’s time to get the data in a proper format to draw actionable insights from it. To do that, we will create a CSV document. Since CSV creator already exists in the PHP library, we do not need external tools or libraries.

Open a file stream in any directory and use fputcsv in each loop of the scraper we created. It will effectively generate a CSV at the end of our program.

$file = fopen("./test.csv", "w");
foreach ($movies as $mId => $movie) {
       fputcsv($file, $arr);

One thing after running this program, we can notice that the CSV file we generated has no column headers. To fix this we put a condition to dump the keys of the array we generated while scraping in the loop just above the fputcsv line.

foreach ($movies as $mId => $movie) {
    if ($mId == 0) {
        fputcsv($file, array_keys($arr));
    fputcsv($file, $arr);

This way at the start of every loop, only at the first movie the key of that array is used to dump a header file at the start of the CSV file.

The entire code will look like this:


require_once "vendor/autoload.php";

use GuzzleHttpClient;
use PHPHtmlParserDom;

$fieldsRequired = [
    'Rank', 'Title', 'Director', 'Leads', 'URL', 'ImageURL', 'Rating', 'NoOfReview', 'ReleaseYear'

$baseUrl = '';
$pageUrl = '/chart/top/?ref_=nv_mp_mv250';
$headers = [
    'user-agent' => 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/ Safari/537.36',
    'accept' => 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8',
    'accept-language' => 'en-US,en;q=0.8'
$file = fopen("./test.csv", "w");

$client = new Client(
    'base_uri' => $baseUrl,
    'headers' => $headers,

$response = $client->request('GET', $pageUrl);
$dom = new Dom();

$movies = $dom->find('table[data-caller-name="chart-top250movie"] > tbody > tr');

foreach ($movies as $mId => $movie) {
    $posterColumn = $dom->find('td.posterColumn');
    $titleColumn = $dom->find('td.titleColumn');
    $ratingColumn = $dom->find('td.ratingColumn.imdbRating');
    $arr = [];
    $arr['Rank'] = trim(array_shift(explode('.', $titleColumn->text)));
    $arr['Title'] = $dom->loadStr($titleColumn)->find('a')->text;
    $names = $dom->loadStr($titleColumn)->find('a')->getAttributes()['title'];
    $arr['Director'] = array_shift(explode(" (dir.), ", $names));
    $arr['Leads'] = array_pop(explode(" (dir.), ", $names));
    $arr['URL'] = $baseUrl.$dom->loadStr($titleColumn)->find('a')->getAttributes()['href'];
    $arr['ImageURL'] = $dom->loadStr($posterColumn)->find('img')->getAttributes()['src'];
    $arr['Rating'] = $dom->loadStr($ratingColumn)->find('strong')->text;
    $ratingText = $dom->loadStr($ratingColumn)->find('strong')->getAttributes()['title'];
    preg_match_all("/[0-9,]+/", $ratingText, $reviews);
    $arr['NoOfReviews'] = str_replace(",", "", array_pop($reviews[0]));
    $arr['ReleaseYear'] = str_replace(["(", ")"], "", $dom->loadStr($titleColumn)->find('span')->text);
    if ($mId == 0) {
        fputcsv($file, array_keys($arr));
    fputcsv($file, $arr);

As a result of our hard-work we will get the following dataset which has all the movies we were looking for. Download the list here.

top rated movies in IMDB
Top IMDB rated movies of all time

Web scraping with PHP is easy (or not!)

Phew! We covered quite a lot of material there, didn’t we? That is basically how you build a crawler, but we need to understand that web processing is designed with users in mind and not for crawlers.

Data extraction when done haphazardly robs expensive processing time from the web servers and harms their business by preventing the actual users from getting the service.

Which has resulted in source websites employing various blocking techniques to prevent the crawlers from sending requests to their servers.

For small-scale projects, you may go ahead and write the crawler yourself. But, as the scope of the project increases, the complications that arise may be too much for a small team to handle, let alone an individual.

Grepsr, with its years of experience in data extraction has the specialty to extract information from the web without compromising the functioning of the web servers. Read about the legality of web scraping here:

We hope you now have the basic know-how to build a web scraper with PHP. If ever you feel the need to expand your data extraction efforts, don’t hesitate to give us a call. We are happy to help.

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Unlock the Power of No-Code Web Scraping: Transform Your Business with Data-Driven Success. Learn how web scraping and external data providers can revolutionize your industry. Explore real-world examples and discover the simplicity of harnessing valuable data.


Drive Success with Car Rental Data Extraction

Tap into the capabilities of car rental data extraction with Grepsr. Outperform competitors, fine-tune fleet management, and just do more.


The Web Scraping Dilemma: Cloud vs. Local Data Extraction

Discover the key differences between cloud and local data extraction methods. Learn how Grepsr can be your guiding star in the world of web scraping.

POI data enrichment

The Power of Web Scraping: Enriching POI Datasets

Discover how web scraping is revolutionizing the extraction and enrichment of POI data, ensuring accuracy and timeliness


Customer Sentiment Analysis and the Role of Web Scraping

Web scraping is indispensable for any Customer Sentiment Analysis Project. Learn how you can leverage web scraping to your advantage.

Mastering Data Visualization in Python with Grepsr’s Data

In a world where data reigns supreme, the ability to make sense of the overwhelming volume of information is nothing short of a superpower. Harnessing the power of data visualization in Python is a superpower in itself. From interactive charts and graphs to immersive dashboards, visualization helps businesses and individuals gain insights from data.  But […]


Extracting Data from Websites to Excel: Web Scraping to Excel

Web scraping and Excel go hand in hand. After extracting the data from the web, you can then organize this data in Excel to capture actionable insights. The internet, by far, is the biggest source of information and data. Juggling through multiple sites to analyze data can be quite irksome. If you are analyzing vast […]

in-house vs external service provider

Five Reasons Why You Need an External Data Provider

Web data extraction of large datasets is almost impossible with in-house capabilities. Learn why you need an external data provider.


Analyzing US Job Postings Data to Understand Job Market & Economy

Leveraging one of Grepsr’s job postings data projects to gather insights — the hottest industries and employers, including working conditions

Web Scraping for Lead Generation: Open a Portal to Sales

Reaching out to leads and converting them into customers doesn’t have to be a shot in the dark. Web scraping can help you get access to high-quality leads databases and scale your lead generation process.

web scraping data solution

Web Scraping: An Unlikely Data Solution

Data has now become something of a currency in the twenty-first century. But, when you think of data, does web scraping come to your mind?  We’re here to tell you it should.

real estate prospecting

Zero-in on Your Real Estate Prospects with Data

Big Data technologies make real estate prospecting more credible and effective by giving you access to real-time web data. You can use web scraping to gather actionable web data and analyze the real estate market environment on a city block level.

web scraping with python

Web Scraping with Python: A How-To Guide

Most businesses (and people) today more or less understand the implications of data on their business. ERP systems enable companies to crunch their internal data and make decisions accordingly. Which would have been enough by and itself if the creation of web data did not rise exponentially as we speak. Some sources estimate it to […]

service better than tools

Why Data Extraction Services are Better Than Tools for Enterprises

The key factors that set a data extraction service apart from its do-it-yourself variant

grepsr partners with datarade

Press Release: Grepsr joins Data Commerce Cloud (DCC) to meet global need for actionable, on-demand DaaS solutions

Dubai, UAE / Berlin, Germany. 1 December 2022 – Grepsr, provider of custom web-scraped data, has become a Premium Partner of Datarade’s Data Commerce Cloud™, the platform which makes data commerce easy. Grepsr’s data products are now available to buy on Datarade Marketplace and other DCC sales channels. Grepsr processes 500M+ records, parses 10K+ web sources, and extracts data […]

Screen Scraping: 4 Important Questions for Scoping your Web Project

Screen scraping should be easy. Often, however, it’s not. If you’ve ever used a data extraction software and then spent an hour learning/configuring XPaths and RegEx, you know how annoying web scraping can get. Even if you do manage to pull the data, it takes way more time to structure it than to make the […]

data in travel & tourism

Significance of Big Data in the Tourism Industry

In a post-pandemic reality, big data helps travel agents and travelers make better decisions, minimize risks, and still have memorable holidays.

Grepsr’s 2021 — A Year in Review

Our growth and achievements of the past year, and reasons to get excited in 2022

web scraping

A Smarter MO for Data-Driven Businesses

Data is key to future-proofing your brand. Web scraping is the first step towards achieving long-term data-driven business success.

data analysis

Business Data Analytics — Why Enterprises Need It

Objectivity vs subjectivity The stories we hear as children have a way of mirroring the realities of everyday existence, unlike many things we experience as adults. An old folk tale from India is one of those stories. It goes something like this: A group of blind men goes to an elephant to find out its […]

data quality

Perfecting the 1:10:100 Rule in Data Quality

Never let bad data hurt your brand reputation again — get Grepsr’s expertise to ensure the highest data quality

data visualization

Data Visualization Is Critical to Your Business — Here Are 5 Reasons Why

Data visualization is a powerful tool. When done correctly, it is a much more elegant method of explaining even complex concepts compared to lengthy texts and paragraphs. Maps and graphs have existed since the 17th century as a means of visualizing data. It was in the mid-1800s that the world saw one the first examples […]

data normalization

What is Data Normalization & Why Enterprises Need it

In the current era of big data, every successful business collects and analyzes vast amounts of data on a daily basis. All of their major decisions are based on the insights gathered from this analysis, for which quality data is the foundation. One of the most important characteristics of quality data is its consistency, which […]

airfare data

Benefits of Using Web Scraping to Extract Airfare Data from OTAs

Use web scraping to extract airfare data from OTAs and airlines’ websites to give your customers the best possible start to their holiday experience.

legality of web scraping

Legality of Web Scraping — An Overview

Ever since the invention of the World Wide Web, web scraping has been one of its most integral facets. It is how search engines are able to gather and display hundreds of thousands of results instantaneously. And also how companies build databases, develop marketing strategies, generate leads, and so on. While its potentials are immense, […]

image scraping

Image Scraping — What is It & How is It Done?

From retail and real estate to tourism and hospitality, images play a vital role in influencing customer decisions. Hence, it is important for brands to see what kinds of photos are turning prospects into customers. On the other side, customers go through numerous products and images before settling on a final choice. Similarly, analysts browse […]

data from alternate sources

Data Scraping from Alternate Sources — PDF, XML & JSON

An unconventional format — PDF, XML or JSON — is just as important a data source as a web page.

QA protocols at Grepsr

QA at Grepsr — How We Ensure Highest Quality Data

Ever since our founding, Grepsr has strived to become the go-to solution for the highest quality service in the data extraction business. In addition to the highly responsive and easy-to-communicate customer service, we pride ourselves in being able to offer the most reliable and quality data, at scale and on time, every single time. QA […]

benefits of high quality data

Benefits of High Quality Data to Any Data-Driven Business

From increased revenue to better customer relations, high quality data is key to your organization’s growth.

quality data

Five Primary Characteristics of High-Quality Data

Big data is at the foundation of all the megatrends that are happening today. Chris Lynch, American writer More businesses worldwide in recent years are charting their course based on what data is telling them. With such reliance, it is imperative that the data you’re working with is of the highest quality. Grepsr provides data […]

11 Most Common Myths About Data Scraping Debunked

Data scraping is the technological process of extracting available web data in a structured format. More businesses globally are realizing the usefulness and potential of big data, and migrating towards data-driven decision-making. As a result, there’s been a huge rise in demand in recent years for tools and services offering data for businesses via Data […]

amazon scraping challenges

Common Challenges During Amazon Data Collection

Over the last twenty years, Amazon has established itself as the world’s largest ecommerce platform having started out as a humble online bookstore. With its presence and influence increasing in more countries, there’s huge demands for its inventory data from various industry verticals. Almost all of the time, this data is acquired via web scraping […]

amazon data extraction

Customer Review Insights: Analyzing Buyer Sentiments of Amazon Products

Actionable insights from Amazon reviews for better decision-making

A Look Back at Grepsr’s 2020

A brief look at Grepsr's achievements in data extraction and industry reach in 2020, and a glimpse into 2021 plans.

Our Newly Redesigned Website is Live!

We’ve redesigned our website to make it easier for you to find what you’re looking for

Preview the New Look Grepsr App

Everybody’s favorite big data tool is getting a fresh coat of paint (and some behind-the-scenes tweaks)

data mining during covid

Role of Data Mining During the COVID-19 Outbreak

How web scraping and data mining can help predict, track and contain current and future disease outbreaks

Grepsr’s 2019 — A Year (and Decade) in Review

Time flies when you’re having fun

Introducing Grepsr’s New Slack-like Support

Making our data acquisition specialists more accessible to busy professionals

Getting an Unstructured Data Error Message? Here’s Why

When you tag data fields using our web scraping browser extension, you may get an error message sometimes that says “The data is unstructured. Please try again.” at the bottom-right corner of the screen. Cause The main reason this happens is that the selected fields are located in different containers within the website’s HTML code. This […]

Introducing Grepsr’s Data Quality Report

Quality assured data to help you make the best business decisions

Report History/Activity on the Grepsr App

A walk-through detailing your report history and how to access (and download) your report’s data from historic crawl runs

Grepsr’s 2018 — A Year in Review

As we say hello to 2019, everyone here at Grepsr firstly wishes our readers and valued customers a very Happy New Year! We look forward to your continued love and support in the new year and beyond. Here’s a look back at some of Grepsr’s highlights in 2018. New Product In addition to our existing […]

Data Retention in Grepsr

New policy announcement

Automate Future Crawls Using Scheduler

Configure and enable schedules to automate future crawls

Data Delivery via Email

Have your Grepsr files automatically delivered by email

Data Delivery via Dropbox

Have your Grepsr files synced automatically to your Dropbox

Data Delivery via FTP

Have your Grepsr files synced automatically to your FTP/SFTP server

Data Delivery via Webhooks

Get notified as soon as your Grepsr data is ready

Data Delivery via Google Drive

Have your Grepsr files synced automatically to your Google Drive

Data Delivery via Amazon S3

Have your Grepsr files synced automatically to your Amazon S3 bucket

Data Delivery via Box

Have your Grepsr files synced automatically to your Box account

Data Delivery via File Feed

Under File Feed, there are two URLs — marked ‘Latest’ and ‘All’. Here’s a brief demo:

Customized Data Extraction via Grepsr Concierge

Although Grepsr for Chrome is a powerful tool in itself, it sometimes lacks the capability to extract data from some websites that are poorly structured, where data fields are hidden, and so on. Here we give you a simple demonstration on how you can get data from these complex websites via our custom platform — Grepsr Concierge. […]

Web Scraping Tutorial for Grepsr Browser Extensions

We designed Grepsr Browser Extensions to make data extraction simple for all of our customers  —  whether they’re technically in tune or not so much.

Common Issues and Tips to Get the Best out of Grepsr

We know how annoying it is when you’ve spent time setting up Grepsr for Chrome to collect your data fields, and then you get back partial or no data at all.

A New Look to the Grepsr App

If you’re a regular Grepsr app user, you may have noticed a slightly modified navigation bar with some new icons at the top of the Grepsr data extraction platform. Previously, all projects would be listed in one place. Now, to make things simpler and more streamlined, we’ve separated the app into two parts based on […]

Grepsr — the Numbers That Matter

Our stats since the start of 2018

Feeds & Endpoint API for Your Data in Grepsr

In our last post, we showed you how to automate your data delivery process in the Grepsr app. This time let’s have a quick look at data feeds and endpoints[*]. Your scraped data’s Endpoint API is the final stop it makes in its journey— starting from the host website, then to your Grepsr account via our crawler, and […]

Automate Your Data Delivery on the Grepsr App

I’m sure you’ve already got the hang of Grepsr for Chrome by now. If you’re like some of our users who are inquiring about data delivery on the app, then this blog is for you! Once you’ve set up your project and the app starts to extract your data, depending on the volume of data requested, it might […]

Two Cool Features You May Have Missed in Grepsr for Chrome

If you’re in constant need of up-to-date and accurate data for your business, chances are you’re using our chrome extension, Grepsr for Chrome, to do the scraping. If you haven’t tried it yet, why haven’t you? It’s fun and easy to use! Although Grepsr for Chrome is already a powerful scraping tool, there might still be a few […]

web scraping with python

Track Changes in Your CSV Data Using Python and Pandas

So you’ve set up your online shop with your vendors’ data obtained via Grepsr’s extension, and you’re receiving their inventory listings as a CSV file regularly. Now you need to periodically monitor the data for changes on the vendors’ side — new additions, removals, price changes, etc. While your website automatically updates all this information when you […]

Kick-Start Your E-commerce Venture with Grepsr

400+ million entrepreneurs worldwide are attempting to start 300+ million companies, according to the Global Entrepreneurship Monitor. Approximately a hundred million new businesses start every year around the world, while a similar number also fold. What sets successful firms apart are the innovations and resources they utilize that help them stay healthy and relevant. Grepsr […]

How to Use Grepsr Browser Tool to Scrape the Web for Free

A beginner’s guide to your favorite DIY web scraping tool Just over a year ago, we introduced the all new Grepsr along with a beta launch of Chrome extension to fill the gap that Kimono Labs, a widely popular scraping tool, left since it’s closure. Now after a year of iteration on both the UI and UX along with shipping […]

Our Kimono Labs Replacement (Grepsr for Chrome) Levels Up

We’ve recently made a number of improvements to make Grepsr for Chrome that little bit easier, and more handy to use. We’ve also received tons of feature requests (keep ’em coming!), so we thought we’d share couple of our favorites that have most recently made it into Grepsr for Chrome. Infinite Scrolling and Enhanced Pagination Support From […]

Welcome To The (New) Grepsr Blog

Hello, Grepsr friends and family, and welcome to the next chapter of Grepsr Blog! It may not look much different yet, but we’re ramping up our editorial operation. Over the next few months you’ll see more posts, more announcements and analysis, more writing, and even new forms of content here. We’re still hammering out all the […]

Introducing the All New Grepsr

Chrome Extension, APIs, Better Support & Much More

Importance of Web Scraping in the Age of Big Data

Big Data has become an internet buzz lately. Not a day goes by without a mention of Big Data in many articles published by media or tech companies around the world.

Data Extraction for BI: Picking the Right Services is Crucial

Finding the appropriate data warehousing and Business Intelligence (BI) platforms that can understand and address your business concerns, priorities, and needs is a daunting task. Specifically, the ones that can have cohesive approaches in generating and deploying your data

Leverage Grepsr to Turn Data into Asset

Have you ever been overwhelmed or even inundated by a sheer amount of data you have to handle every day? Handling too much of data can be a painstaking job in the age that has seen an enormous surge in digitization, quantification, and datafication of information. Today, you have to be equipped with data no […]

Welcoming New Year 2014 with Renewed Energy

2013 in the Retrospect 2013 was a very productive year for Grepsr. Measuring our success as a startup, we were able to maintain a steady progress in this year. We achieved a significant growth in terms of users, orders, and revenues, which was many times larger than 2012. During 2013, we managed to go global, […]

Web Scraping vs API

Every system you come across today has an API already developed for their customers or it is at least in their bucket list. While APIs are great if you really need to interact with the system but if you are only looking to extract data from the website, web scraping is a much better option. […]

Web Crawling Software or Web Crawling Service

Some people ask us if we are a “service” or a “software”. We simply tell them – we are a service, with killer software that runs behind the scenes! 🙂 Also, lot of our customers ask us, why go for a Web Crawling Service over a Web Crawling Software? The answer is pretty straight forward. […]

Managed Data Extraction Service

Grepsr is what we like to call, “Managed Data Extraction Service”. Here are some of the reasons why we call it “managed”: We let you focus on your business and use the data — worrying about technical details of extraction is our job, and we will do it for you. We let you describe your […]

Official Launch of Grepsr (Beta)

We are immensely proud to launch Grepsr today. Grepsr is probably one of the first Web 2.0 Software as a Service (SaaS) products for website data extraction. So what does this mean for the customers? Cheaper costs – you pay a flat monthly fee no matter how big or small your extraction needs are. Fully […]