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Scraping Website: How to Write a Script in Python

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Here's a python script that you can use as a model to scrape a website. Python script The below logic uses BeautifulSoup Package for web scraping. import requests from bs4 import BeautifulSoup url = 'https://www.example.com' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Print the title of the webpage print(soup.title.text) # Print all the links in the webpage for link in soup.find_all('a'):     print(link.get('href')) In this script, we first import the Requests and Beautiful Soup libraries. We then define the URL we want to scrape and use the Requests library to send a GET request to that URL. We then pass the response text to Beautiful Soup to parse the HTML contents of the webpage. We then use Beautiful Soup to extract the title of the webpage and print it to the console. We also use a for loop to find all the links in the webpage and print their href attributes to the console. This is just a basic example, but
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Here's to Know Data lake Vs Database

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In a data lake, data stored internally in a repository. You can call it a blob. The data in the lake a no-format data, but you need a schema for the database. 



Data lake Repository

Database

  • In the database, the Schema definition you need before you store data on it.
  • It should follow Codd's rules.
  • Here data is completely formatted.
  • The data stores here in Tables, so you need SQL language to read the records.
  • Poor performance in terms of scalability.



Data lake

  • It doesn't have any format - it's just a dump.
  • You can send this dump to the Hadoop repository for data analysis.
  • This repository can be incremental. You can build a database.
  • The data lake is a dump of data with no format. It needs a pre-format before it sends for analytics.
  • Data security and encryption: You need these before you send data to Hadoop.
  • In real-time, you need to pre-process data.
  • This data you need to send to the data warehouse to get insights.

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