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Python: Built-in Functions vs. For & If Loops – 5 Programs Explained

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Python’s built-in functions make coding fast and efficient. But understanding how they work under the hood is crucial to mastering Python. This post shows five Python tasks, each implemented in two ways: Using built-in functions Using for loops and if statements ✅ 1. Sum of a List ✅ Using Built-in Function: numbers = [ 10 , 20 , 30 , 40 ] total = sum (numbers) print ( "Sum:" , total) 🔁 Using For Loop: numbers = [ 10 , 20 , 30 , 40 ] total = 0 for num in numbers: total += num print ( "Sum:" , total) ✅ 2. Find Maximum Value ✅ Using Built-in Function: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = max (values) print ( "Max:" , maximum) 🔁 Using For and If: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = values[ 0 ] for val in values: if val > maximum: maximum = val print ( "Max:" , maximum) ✅ 3. Count Vowels in a String ✅ Using Built-ins: text = "hello world" vowel_count = sum ( 1 for ch in text if ch i...

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...