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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:...

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.