Trending March 2024 # Web Scraping Vs Data Mining: Why The Confusion? # Suggested April 2024 # Top 12 Popular

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Web scraping is the process of scanning the text or multimedia content from targeted websites and turning this content into data table that can be analyzed. So essentially, web scraping is a form of data extraction. It does not generate any business insights before the collected data is cleaned, formatted and analyzed.

Just like the example we mentioned, a very common use case web scraping enables data mining is commercial data on e-commerce business owners or brands that provide an online shop. Web scraping tools can collect the product definition, reviews, price, features, stock status, colors, ratings, and many other information that can generate insight for businesses. Apart from good and products, web scraping can also collect service information such as flight fares, ticket prices and freelancer fees across all the websites you target.

Natural language processing as a data mining method has transformed text data into a valuable asset. Web scraping is a fast and efficient way to collect written data on web. It can scrape entire articles, tables and images on the articles as well as links that are embedded in these articles. It can target exact websites or top search engine results that appear for a certain keyword.

In one second, there are more than 9000 tweets on Twitter and 1000 Instagram posts on Instagram on average. Depending on what your industry you are in, a significant amount of this big and increasing content can be relevant to your business. Web scraping can target certain keywords and hashtags that are important to your business into the data of what people said online. This data can reveal whether there is more activity on social media for your competitors, whether your consumers mention negative or positive words about your product, and many other insights about new trends emerging.

If you already have existing data mining processes supporting your business decisions or plan to use new methods, you can access free data sources scraped from the web to see whether any of the use cases we mentioned above can be beneficial for your business. Keep in mind that if you decide using web scraping on a continuous basis, you need to consider all the benefits and challenges of collecting data from the web before making a decision on whether you’d like to build such a capability in-house or leverage an external provider.

Sponsored:

One way to find free data sets that may be more suitable for commercial use cases can by getting a sample from Bright Data. They already have readily available datasets that are up-to-date and collected for specific use cases which may help you run proof of concept analysis and decide whether web scraping is a useful tool for your business.

Recommended Reading:

To explore web scraping use cases for different industries, its benefits, and challenges read our articles:

If you want to have more in-depth knowledge about web scraping, download our whitepaper:

If you believe that your business may benefit from a web scraping solution, check our list of web crawlers to find the best vendor for you.

For guidance to choose the right tool, reach out to us:

This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Data Mining Vs Data Warehousing: 8 Critical Differences

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Providing insights into the trends, prediction, and appropriate strategy for the company and serving numerous other uses are distinct. Both are associated with data usage but differ from each other. Let’s explore the distinctive features of data mining vs data warehousing in different aspects, such as characteristics, functionalities, challenges, applications, and others.

What is Data Warehousing?

Data warehousing is the data organization and compilation method into a single database for efficient, effortless, centralized usage. It refers to copying data from different organization systems for further processing, such as data cleaning, integration and consolidation. It aids in maintaining the accuracy, consistency and quality of the data and avoids redundancy. Data warehousing also includes sorting the data into a recognizable pattern to interpret its type and format. The data characteristics are non-volatile, integrated, time-variant and subject-oriented data. The processing at the data warehouse is as follows: 

Source → Extract → Transform → Load → Target

What is Data Mining? 

Data mining is the data analysis and extraction method to fetch the information, the functional aspect of the input. It involves predictive analysis and different aspects such as statistics, artificial intelligence, machine learning, natural language processing, etc. Data mining is associated with extracting valid, hidden and useful information that might be previously unknown. The practical application includes fraud detection, building risk models, scientific discovery and trend analysis. 

Data Mining vs Data Warehousing

CriteriaData MiningData WarehousingDefinitionProcess of discovering patterns in large datasetsProcess of collecting, storing and managing data from various sourcesPurposeTo extract useful insights and knowledge from dataTo provide a comprehensive view of an organization’s dataFocusAnalyzing data to identify patterns, correlations and trendsStorage and management of data for reporting and analysisSource of dataLarge datasets from various sourcesMultiple sources, including internal and external systemsData processingAdvanced techniques like machine learning algorithmsAggregating, transforming and organizing dataAnalysis methodsTechniques such as clustering, classification and regressionQueries, reports and online analytical processing (OLAP)TimeframeHistorical and current dataHistorical data only

1. Objectives and Focus

The data warehouse and data mining difference concerning objectives and focus is as follows: 

Data Warehousing: Centralized Data Storage and Management

Data warehousing is a storage system that holds much data in one place. Its main goal is to make finding and analyzing the data easy and efficient. Everyone in an organization can access the data to help with their work.

Data Mining: Knowledge Discovery and Pattern Identification

The unprocessed and raw data only hold significance after being processed and that’s how data mining comes into play. It aims to discover the potential of the data for problem-solving and decision-making. It identifies the patterns and relationships and provides output as information.

Source: Hevodata

2. Data Sources and Integration

The difference between data mining and data warehousing in data sources and integration is explained below: 

Data Warehousing: Integration of Structured and Organized Data

Do you think data originates from a single source? No! Data gathering happens from multiple sources, such as applications, organization systems and databases. The integration process involves data extraction and transformation into a specific structured data format, and further sorting of this data is Data Warehousing. The structured and organized data are available in easily interpretable forms such as tables, rows and columns. Serving numerous benefits, data warehousing thus involves the extraction of data from different sources and conversion into the required format for better usefulness.

Data Mining: Extraction of Patterns from Diverse Data Sources

Diverse data sources include data available in unstructured, semi-structured and structured formats. The source of data mining includes sensor data, text documents, databases, social media feeds and other such sources. Based on the query, the relevant data is searched for to gain informational insights into raw and unprocessed data, derivation of relationships and discovery of hidden patterns through statistical analysis and machine learning.

3. Data Structure and Granularity

The data structure and granularity are other aspects that differentiate between data mining and data warehousing: 

Data Warehousing: Aggregated and Summarized Data for Analysis

Data warehousing provides the option to return reports of queries from data. The process is made easy through the accumulation of aggregated and summarized data. It includes using various tools like query and reporting, data visualization, business intelligence, and online analytical processing (OLAP) tools. Common examples of these tools include SQL, Tableau, Oracle Essbase, SAP business objects, Qlik view, SAP business warehouse, IBM Cognos, and others. 

Data Mining: Detailed and Granular Data Exploration

It includes analysis of each data such as transactions, records and events at granular and detailed levels to find unrecognizable patterns at aggregated levels. The applications are primarily beneficial in analyzing complex datasets, deriving logical interpretations from them, and ensuring efficient use of customer data by understanding their behavior and making further predictions. It requires the usage of programming languages like R and Python. Further data processing frameworks like Apache Spark, data science platforms like Rapid Miner, and visualization tools like KNIME find proficient use in the process. 

The difference between data mining and data warehousing in analytics techniques and tools is enlisted below: 

Data Warehousing: OLAP for Reporting

OLAP is significantly involved in reporting and analysis of aggregated data. It is a complex of tools and techniques that performs specific functions. For instance, OLAP cubes are concerned with storage and data organization for analysis, and multidimensional data model functions to data organization into dimensions and measures. It is also responsible for granularity at different levels and allows the selection of specific data subsets by selecting values from different dimensions. Slice and dice operation of OLAP performs the later. 

Data Mining: ML Algorithms and Statistical Analysis

Machine learning algorithms are associated with discovering hidden patterns, relationships and data potential. The algorithms are categorized into groups depending on their functionality. The different categories involve classification, association rule mining, clustering and regression. They can predict results, make data-driven decisions and recognize the association among data from different sources. 

Concerning statistics, descriptive and inferential statistics, correlation analysis and hypothesis testing are of significance in data mining. They measure the importance, check the accuracy, validate results, and quantify the relationships. 

Let us differentiate between data mining and data warehousing with respect to time dependency and data updates below: 

Data warehousing: Supports Historical and Periodic Data Snapshots

A high volume of companies rely on periodical data for their functionality. It regularly raises data storage requirements and creates a timeline with easy access to different periods. The easy access helps in analysis and comparison to identify the trends and patterns. The scheduled data refresh options allow automatic data updates from various sources and segregate the data through data partitioning techniques. It is beneficial in imparting speedy operation, retrieval and analysis.

Source: Wall Street mojo

Data Mining: Focuses on Real-time and Dynamic Data Analysis

Data mining also considers time-dependent data analysis through action over real-time data streams and dynamic datasets such as financial market data, sensor data and social media feeds. It involves streaming analytics that refers to non-stop analysis of continuously flowing data. The data mining is also automated to update the specific new data rather than processing a complete data set. Further, event-based data detection and analysis also help find information from the dynamic data.

6. Usage and Applications

Based on the applicability, the difference between data mining and data warehousing is:

Data Warehousing: Strategic Decision-making and Trend Analysis

Two important factors for data warehousing are decision-making and trend analysis. It provides combined information based on time aspects that allows trend analysis. It helps in pattern identification, which provides the base to formulate a strategy and guide the company toward success.

Data Mining: Predictive Modeling, Market Segmentation, Fraud Detection

It also uses historical data to build predictive models directly applied to trend analysis. It functions by understanding customer behavior and their demands. Data mining supports target-based marketing, where its application of understanding consumer characteristics and preferences plays a crucial role. Utilizing the same features also allows fraud detection based on the history and customer’s identity.

7. Relationship and Integration

Data mining vs data warehousing in regards to relationship and integration: 

Data Warehousing Provides the Foundation for Data Mining

The data collection required to interpret information is found at the data warehouse. It provides specifically formatted data that is easy to work on and visualize. The storage after the accumulation and processing of data helps in the anywhere and anytime functionality of data mining. 

Data Mining Leverages Data from Data Warehousing Systems

Data mining is processing information from the accumulated data. A Data warehouse is a single platform containing information from multiple and distinct sources. The processed, cleansed and transformed data is easy to retrieve and further used for analysis. 

8. Challenges and Considerations

According to the data mining vs data warehousing challenges and considerations, here are some points worth viewing:

Data Quality and Consistency in Data Warehousing

Data quality and consistency is a challenging tasks in data warehousing. The data from different formats, quality, and structures require additional processes such as data duplication, normalization and resolution of inconsistencies. It also involves data cleansing and governance by establishing practices and policies for best practices.

Interpretation and Validation of Data Mining Results

The data has to be interpreted repeatedly according to different contexts. A clear understanding of the problem statement is crucial for accurate results. Cross-validation and verification are crucial while performing data mining owing to sometimes the production of overfitting and biased results. Humans are also assigned to check generated data’s practical applicability and relevance due to often witnessed discrepancies.

Final Verdict

To sum up, regardless of both dealing with data, warehousing and mining are apart from each other. While the former provides a foundation and base for the functionality of data mining, the latter is crucial to impart meaning to warehouse constituents. Data warehousing is responsible for data quality, accessibility, and consistency. Similarly, data mining is associated with leveraging the stored to help guide the company to success. Data mining vs data warehousing hence finds itself distinct yet related to each other while serving the organizations, research and market. If you want to learn both the techniques then our Blackbelt program is the best option for you. Explore the program today!

Frequently Asked Questions

Q1. What is the difference between data and data warehouse?

A. Data refers to any formatted information, while a data warehouse is a centralized data repository used for analysis and reporting.

Q2. What are the different types of data warehouse and data mining?

A. The different types of data warehouses include enterprise data warehouses, operational data stores, and data marts. Different data mining techniques include classification, clustering, regression, and association rule learning.

Q3. What is the difference between data mining and ETL?

A. ETL (extract, transform, load) is moving data from various sources into a data warehouse, while data mining is discovering patterns in large datasets.

Q4. What is the difference between database and data mining?

A. A database is a collection of structured data organized for efficient storage and retrieval, while data mining is analyzing data to extract insights or patterns.

Related

Identity Cohesion Vs. Role Confusion

Everyone knows the term “identity crisis,” especially when they are teenagers, and it is a dramatic phase and carries a negative connotation. Some psychologists, however, believe it is a normal part of social development. Erik Erikson formulated a theory called the eight psychosocial stages of development which he describes this phase in his fifth stage, identity cohesion vs. role confusion.

Identity Cohesion vs. Role Confusion Erik Erikson’s Theory- Identity Crisis

The German-American psychologist Erik Erikson coined the term “identity crisis.” Some psychologists say that Erikson did so because of his turbulent childhood. He was born in Germany in 1902 and adopted by a Jewish stepfather. He often felt like an outsider and was teased for being Hewish. As he experienced identity crises between the ages of 12–18, this may be why, he speculated, his struggle was present in other children as well.

What does Happen Before Identity Cohesion vs. Role Confusion? The 5th Stage: Identity Cohesion vs. Role Confusion

Now, during adolescence, the individual questions these prior identifications. Then the ego reconstructs them while integrating these with strong, emerging sexual feelings and social roles.

What is Identity?

Identity is a multifaceted concept- it refers to the sense of uniqueness one gets from various psychosocial experiences. The ego integrates all the identifications learned as participants in different groups and all self-images. It includes feelings of making good partner choices and our connection with the future when opting for certain careers. This is important in establishing who the individual is in a social setting. Therefore, identity consists of what the individual is, what they want to become, and what they are supposed to be.

Understanding Identity Crisis

Identity cannot be easily achieved; an identity crisis is required. This is the connection between childhood and adulthood. Here, individuals must solve multiple special problems, and improper resolution of these problems forces the individual to find an identity again in later stages. These crises in youths stem from role confusion. Here, they experience a tough period of self-consciousness characterized by awakening sexual drives and body growth. This brings doubts and shame over what they are and may become. The most troublesome part of this stage is deciding one’s occupational identity. Though individuals want to commit to goals that give meaning to their lives, many find it extremely difficult.

The Concept of Totalism

Erikson gave that confused individuals often try to establish their identities by over identifying with their heroes, making them defensive in many ways. Thus, their behavior during this phase is characterized by totalism, which refers to setting absolute boundaries regarding one’s values, beliefs, and interpersonal relationships. Here, simple ideologies may be embraced with little questioning.

Erikson thought these behaviors should be viewed as alternative ways of dealing with experiences. His view on development is optimistic, and he believes that such failure to behave constructively results from political, cultural, and technological changes that lead to establishing values that no longer work. Here, the role of mentors and role models is crucial. Adults need to help individuals with unclear values and provide them with guidance. They need to emulate behaviors that are socially good and acceptable. Remember, both generations need each other to survive.

Conclusion

A Simple Introduction To Web Scraping With Beautiful Soup

The purpose of this series is to learn to extract data from websites. Most of the data in websites are in HTML format, then the first tutorial explains the basics of this markup language. The second guide shows a way to scrape data easily using an intuitive web scraping tool, which doesn’t need any knowledge of HTML. Instead, the last tutorials are focused on gathering data with Python from the web. In this case, you need to grasp to interact directly with HTML pages and you need some previous knowledge of it.

The post is the fourth in a series of tutorials to build scrapers. Below, there is the full series:

As an example, I am going to parse a web page using two Python libraries, Requests and Beautiful Soup. The list of countries by greenhouse gas emissions will be extracted from Wikipedia as in the previous tutorials of the series.

You surely aren’t allowed to scrape data from all the websites. I recommend you first look at the chúng tôi file to avoid legal implications. You only have to add ‘/robots.txt’ at the end of the URL to check the sections of the website allowed/not allowed.

Web scraping is the process of collecting data from the web page and store it in a structured format, such as a CSV file. For example, if you want to predict the Amazon product review’s ratings, you could be interested in gathering information about that product on the official website.

1. Import libraries

The first step of the tutorial is to check if all the required libraries are installed:

!pip install beautifulsoup4 !pip install requests

Once we terminated to look, we need to import the libraries:

Let’s import:

from bs4 import BeautifulSoup import requests import pandas as pd

Beautiful Soup is a library useful to extract data from HTML and XML files. A sort of parse tree is built for the parsed page. Indeed, an HTML document is composed of a tree of tags. I will show an example of HTML code to make you grasp this concept.

Illustration by Author

Since the HTML has a tree structure, there are also ancestors, descendants, parents, children and siblings.

2. Create Response Object

To get the web page, the first step is to create a response object, passing the URL to the get method.

req = requests.get(url) print(req) Request-Response Protocol. Illustration by Author.

This operation can seem mysterious, but with a simple image, I show how it works. The client communicates with the server using a HyperText Transfer Protocol(HTTP). In this line of code, it’s like when we type the link on the address bar, the browser transmits the request to the server and then the server performs the requested action after it looked at the request.

3. Create a Beautiful Soup object

Let’s create the Beautiful Soup object, which parses the document using the HTML parser. In this way, we transform the HTML code into a tree of Python objects, as I showed before in the illustration.

soup = BeautifulSoup(req.text,"html.parser") print(soup)

If you print the object, you’ll see all the HTML code of the web page.

4. Explore HTML tree

As you can observe, this tree contains many tags, which contain different types of information. We can get access directly to the tags, just writing:

soup.head soup.body soup.body.h1

A more efficient way is to use the find and find_all methods, which filter the element(s in case of find_all method).

row1 = tab.find('tr') print(row1)

Using the find method, we zoom a part of the document within the

tags, which are used to build each row of the table. In this case, we got only the first row because the function extracts only one element. Instead, if we want to gather all the rows of the table, we use the other method:

rows = tab.find_all('tr') print(len(rows)) print(rows[0])

We obtained a list with 187 elements. If we show the first item, we’ll see the same output as before. find_all method is useful when we need to zoom in on more parts with the same tag within the document.

5. Extract elements of the table

To store all the elements, we create a dictionary, which will contain only the names of the columns as keys and empty lists as values.

rows = tab.find_all('tr') cols = [t.text.rstrip() for t in rows[0].find_all('th')] diz = {c:[] for c in cols} print(diz)

So, we iterate over the rows of the table, excluding the first:

for r in rows[1:]: diz[cols[0]].append(r.find('th').text. replace('xa0', '').rstrip()) row_other = r.find_all('td') for idx,c in enumerate(row_other): cell_text = c.text.replace('xa0', '').rstrip() diz[cols[idx+1]].append(cell_text)

The first column is always contained within the

tags, while the other columns are within the

tags. To avoid having “n” and “xa0”, we use respectively the rstrip and replace functions.

In this way, we extract all the data contained in the table and save it into a dictionary. Now, we can transform the dictionary into a pandas DataFrame and export it into a CSV file:

df = pd.DataFrame(diz) df.head() df.to_csv('tableghg.csv')

Finally, we can have an overview of the table obtained. Isn’t it amazing? And I didn’t write many lines of code.

Final thoughts

I hope you found useful this tutorial. Beautiful Soup can be the right tool for you when the project is small. On the other hand, if you have to deal with more complex items in a web page, such as Javascript elements, you should opt for another scraper, Selenium. In the last case, it’s better to check the third tutorial of the series. Thanks for reading. Have a nice day!

Scraping Data Using Octoparse For Product Assessment

In today’s data-driven world, it is crucial to have access to reliable and relevant data for informed decision-making. Often, data from external sources is obtained through processes like pulling or pushing from data providers and subsequently stored in a data lake. This marks the beginning of a data preparation journey where various techniques are applied to clean, transform, and apply business rules to the data. Ultimately, this prepared data serves as the foundation for Business Intelligence (BI) or AI applications, tailored to meet individual business requirements. Join me as we dive into the world of data scraping with Octoparse and discover its potential in enhancing data-driven insights.

This article was published as a part of the Data Science Blogathon.

Web Scrapping and Analytics

Yes! In some cases, we have e to grab the data from an external source using Web Scraping techniques and do all data torturing on top of the data to find the insight of the data with techniques.

Same time we do not forget to use to find the relationship and correlation between features and expand the other opportunities to explore further by applying mathematics, statistics, and visualisation techniques, on top of selecting and using machine learning algorithms and finding the prediction/classification/clustering to improve the business opportunities and prospects, this is a tremendous journey.

Focusing on excellent data collection from the right resource is the critical success of a data platform project. I hope you know that. In this article, let’s try to understand the process of gaining data using scraping techniques – zero code.

Before getting into this, I will try to understand a few things better.

Data Providers

As I mentioned earlier, the Data Sources for DS/DA could be in from any data source. Here, our focus is on Web-Scraping processes.

What is Web-Scraping and Why?

Web-Scraping is the process of extracting data in diverse volumes in a specific format from a website(s) in the form of slice and dice for Data Analytics and Data Science standpoint and file formats depending on the business requirements. It would .csv, JSON, .xlsx,.xml, etc.. Sometimes we can store the data directly into the database.

Why Web-Scraping?

Web-Scraping is critical to the process; it allows quick and economical extraction of data from different sources, followed by diverse data processing techniques to gather the insights directed to understand the business better and keep track of the brand and reputation of a company to align with legal limits.

Web Scraping Process RequestVsResponse

The first step is to request the target website(s) for the specific contents of a particular URL, which returns the data in a specific format mentioned in the programming language (or) script.

Parsing&Extraction

As we know, Parsing is usually applied to programming languages (Java..Net, Python, etc.). It is the structured process of taking the code in the form of text and producing a structured output in understandable ways.

Data-Downloading

The last part of scrapping is where you can download and save the data in CSV, JSON format or a database. We can use this file as input for Data Analytics and Data Science perspective.

There are multiple Web Scraping tools/software available in the market, and let’s look at a few of them.

In the market, many Web-Scraping tools are available, and let’s review a few of them.

ProWebScraper Features

Completely effortless exercise

It can be used by anyone can who knows how to browse

It can scrape Texts, Table data, Links, Images, Numbers and Key-Value Pairs.

It can scrape multiple pages.

It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)

Highly scalable, it can run multiple scrapers simultaneously and thousands of pages.

Let’s focus on Octoparse,

The web-Data Extraction tool, Octoparse, stands out from other devices in the market. You can extract the required data without coding, scrape data with modern visual design, and automatically scrapes the data from the website(s) along with the SaaS Web-Data platform feature.

Octoparse provides ready-to-use Scraping templates for different purposes, including Amazon, eBay, Twitter, Instagram, Facebook, BestBuy and many more. It lets us tailor the scraper according to our requirements specific.

Compared with other tools available in the market, it is beneficial at the organisational level with massive Web- Scraping demands. We can use this for multiple industries like e-commerce, travel, investment, social, crypto-currency, marketing, real estate etc.

Features

Both categories could find it easy to use this to extract information from websites.

ZERO code experience is fantastic.

Indeed, it makes life easier and faster to get data from websites without code and with simple configurations.

It can scrape the data from Text, Table, Web-Links, Listing-pages and images.

It can download the data in CSV and Excel formats from multiple pages.

It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)

Excellent API integration feature, which delivers the data automatically to our systems.

Now time to Scrape eBay product information using Octoparse.

Getting product information from eBay, Let’s open the eBay and select/search for a product, and copy the URL

In a few steps, we were able to complete the entire process.

Open the target webpage

Creating a workflow

Scrapping the content from the specified web pages.

Customizing and validating the data using review future

Extract the data using workflow

Scheduling

Open Target Webpage

Let’s login Octoparse, paste the URL and hit the start button; Octoparse starts auto-detect and pulls the details for you in a separate window.

Creating Workflow and New-Task

Wait until the search reaches 100% so that you will get data for your needs.

During the detection, Octoparse will select the critical elements for your convenience and save our time.

Note: To remove the cookies, please turn off the browser tag.

Scrapping the Content from the Identified Web-page

Once we confirm the detection, the Workflow template is ready for configurations and data preview at the bottom. There you can configure the column as convenient (Copy, Delete, Customize the column, etc.,)

Customizing and Validating the Data using Review Future

You can add your custom field(s) in the Data preview window, import and export the data, and remove duplicates.

Extract the Data using Workflow

On the Workflow window, based on your hit on each navigation, we could move around the web browser. – Go to the web page, Scroll Page, Loop Item, Extract Data, and you can add new steps.

We can configure time out, file format in JSON or NOT, Before and After the action is performed, and how often the action should perform. After the required configurations have been done, we could act and extract the data.

Save Configuration, and Run the Workflow

Schedule-task

You can run it on your device or in the cloud.

Data Extraction – Process starts

Data ready to Export

Chose the Data Format for Further Usage

Saving the Extracted Data

Extracted Data is Ready in the Specified-format

Data is ready for further usage either in Data Analytics and Data Science

What is Next! Yes, no doubt about that, have to load in Jupiter notebook and start using the EDA process extensively.

Conclusion

Importance of Data Source

Data Science Lifecycle

What is Web Scrapping and Why

The process involved in Web Scrapping

Top Web Scraping tools and their overview

Octoparse Use case – Data Extraction from eBay

Data Extraction using Octoparse – detailed steps (Zero Code)

I have enjoyed this web-scraping tool and am impressed with its features; you can try and want it to extract free data for your Data Science & Analytics practice projects perspective.

Frequently Asked Questions

Related

Roadmap To Web Scraping: Use Cases, Methods & Tools In 2023

Data is critical for business and internet is a large data source including insights about vendors, products, services, or customers. Businesses still have difficulty automatically collecting data from numerous sources, especially the internet. Web scraping enables businesses to automatically extract public data from websites using scraping tools.

In this article, we will dive into each critical aspect of web scraping, including what it is, how it works, its use cases and best practices.

What is web scraping?

Web scraping, sometimes called web crawling,  is the process of extracting data from websites.  

The process of scraping a page involves making requests to the page and extracting machine-readable information from it. As seen in figure 2 general web scraping process consists of the following 7 steps :

Identification of target URLs

If the website to be crawled uses anti-scraping technologies such as CAPTCHAs, the scraper may need to choose the appropriate proxy server solution to get a new IP address to send its requests from.

Making requests to these URLs to get HTML code

Using locators to identify the location of data in HTML code

Parsing the data string that contains information

Converting the scraped data into the desired format

Transferring the scraped data to the data storage of choice

Figure 2: 7 steps of an web scraping process

Sponsored

Bright Data offers its web scraper as a managed cloud service. Users can rely on coding or no-code interfaces to build scrapers that run on the infrastructure provided by their SaaS solution.

Which web crawler should you use?

The right web crawler tool or service depends on various factors, including the type of project, budget, and technical personnel availability. The right-thinking process when choosing a web crawler should be like the below:

We developed a data-driven web scraping vendor evaluation to help you selecting the right web scraper.

Figure 3: Roadmap for choosing the right web scraping tool

Top 10 web scraping applications/use cases Data Analytics & Data Science

1. Training predictive models: Predictive models require a large volume of data to improve the accuracy of outputs. However, collecting a large volume of data is not easy for businesses with manual processes. Web crawlers help data scientists extract required data instead of doing it manually.

2. Optimizing NLP models: NLP is one of the conversational AI applications. A massive amount of data, especially data collected from the web, is necessary for optimizing NLP models. Web crawlers provide high-quality and current data for NLP model training.

Real Estate

3. Web scraping in real estate: Web scraping in real estate enables companies to extract property and consumer data. Scraped data helps real estate companies:

Oxylabs’ real estate scraper API allows users to access and gather various types of real estate data, including price history, property listings, and rental rates, bypassing anti-bot measures. 

Marketing & sales

4. Price scraping: Companies can leverage crawled data to improve their revenues. Web scrapers automatically extract competitors’ price data from websites. Price scraping enables businesses to: 

6. Lead generation: Lead generation helps companies improve their lead generation performances, time and resources. More prospects data is available online for B2B and B2C companies. Web scraping helps companies to collect the most up-to-date contact information of new customers to reach out to, such as social media accounts and emails.

7. SEO monitoring: Web scraping helps content creators check primary SEO metrics, such as keywords ranking, dead links, rank on the google search engine, etc. Web crawlers collect publicly available competitor data from targeted websites, including keywords, URLs, customer reviews, etc. Web crawlers enable companies to optimize their content to attract more views.

8. Market sentiment analysis: Using web scrapers in marketing enables companies:

9. Improving recruitment processes: Web scrapers help recruiters automatically extract candidates’ data from recruiting websites such as LinkedIn. Recruiters can leverage the extracted data to: 

analyze and compare candidates’ qualifications.

collect candidates’ contact information such as email addresses, and phone numbers.

collect salary ranges and adjust their salaries accordingly, 

analyze competitors’ offerings and optimize their job offerings.

Finance & Banking

10. Credit rating: The process of evaluating the credit risk of a borrower’s creditworthiness. Credit scores are calculated for an individual, business, company, or government. Web scrapers extract data about a business’s financial status from company public resources to calculate credit rating scores.

Check out top 18 web scraping applications & use cases to learn more about web scraping use cases. 

Top 7 web scraping best practices

 Here you can find top 7 web scraping best practices that help you to imply web scraping:

Complex website structures: Most web pages are based on HTML, and web page structures are widely divergent. Therefore when you need to scrape multiple websites, you need to build one scraper for each website.

Scraper maintenance can be costly: Websites change the design of the page all the time. If the location of data that is intended to be scrapped changes, crawlers are required to be programmed again.

Anti-scraping tools used by websites: Anti-scraping tools enable web developers to manipulate content shown to bots and humans and also restrict bots from scraping the website. Some anti-scraping methods are IP blocking, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), and honeypot traps.

Login requirement: Some information you want to extract from the web may require you to log in first. So when the website requires login, the scraper needs to make sure to save cookies that have been sent with the requests, so the website recognizes the crawler is the same person who logged in earlier.

Slow/ unstable load speed: When websites load content slowly or fail to respond, refreshing the page may help, yet, the scraper may not know how to deal with such a situation.

To learn more about web scraping challenges, check out web scraping: challenges & best practices

For more on web scraping

If you still have any questions about web scraping, how it works, use cases, benefits, and challenges, feel free to read our in-depth whitepaper on the topic:

If you still have questions about the web scraping landscape, feel free to check out the sortable list of web scraping vendors.

You can also contact us:

This article was originally written by former AIMultiple industry analyst Izgi Arda Ozsubasi and reviewed by Cem Dilmegani

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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