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Python Set Operations Explained: From Theory to Real-Time Applications

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A  set  in Python is an unordered collection of unique elements. It is useful when storing distinct values and performing operations like union, intersection, or difference. Real-Time Example: Removing Duplicate Customer Emails in a Marketing Campaign Imagine you are working on an email marketing campaign for your company. You have a list of customer emails, but some are duplicated. Using a set , you can remove duplicates efficiently before sending emails. Code Example: # List of customer emails (some duplicates) customer_emails = [ "alice@example.com" , "bob@example.com" , "charlie@example.com" , "alice@example.com" , "david@example.com" , "bob@example.com" ] # Convert list to a set to remove duplicates unique_emails = set (customer_emails) # Convert back to a list (if needed) unique_email_list = list (unique_emails) # Print the unique emails print ( "Unique customer emails:" , unique_email_list) Ou...
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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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