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Python Logic to Find All Unique Pairs in an Array

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 Here's the Python logic for finding all unique pairs in an array that sum up to a target value. Python Unique Pair Problem Write a Python function that finds all unique pairs in an array whose sum equals a target value. Avoid duplicates in the result. For example: Input: arr = [2, 4, 3, 5, 7, 8, 9] , target = 9 Output: [(2, 7), (4, 5)] Hints Use a set for tracking seen numbers. Check for complements efficiently. Example def find_unique_pairs(arr, target):     """     Finds all unique pairs in the array that sum up to the target value.     Parameters:     arr (list): The input array of integers.     target (int): The target sum value.     Returns:     list: A list of unique pairs that sum to the target value.     """     seen = set()     pairs = set()     for num in arr:         complement = target - num         if complement in seen:...
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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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