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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...

How Hadoop is Better for Legacy data

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Here is an interview question on legacy data. You all know that a lot of data is available on legacy systems. You can use Hadoop to process the data for useful insights. 1. How should we be thinking about migrating data from legacy systems? Treat legacy data as you would any other complex data type.  HDFS acts as an active archive, enabling you to cost-effectively store data in any form for as long as you like and access it when you wish to explore the data. And with the latest generation of data wrangling and ETL tools, you can transform, enrich, and blend that legacy data with other, newer data types to gain a unique perspective on what’s happening across your business. 2. What are your thoughts on getting combined insights from the existing data warehouse and Hadoop? Typically one of the starter use cases for moving relational data off a warehouse and into Hadoop is active archiving.  This is the opportunity to take data that might have otherwise gone to the archive and k...