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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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The Perfect Way to Swap two Strings in Python

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Here is the perfect way swap two strings in python. Without a third variable, you can swap strings in Python. With the swap function, you can achieve this. Here's the sample logic. Swap two strings Multiple arguments you can use in the same function. Here,  a  and b  are arguments for the swap function. You'll get output as swapped when you use the swap function. def swap(a, b):  return b,a  Logic to swap strings. i = "Hello world" j = "This is ApplyBigAnalytics"  (i, j) = swap(i, j)  print(i)  print(j)  Logic to Swap two numbers. i = 1  j = 2  (i, j) = swap(i,j)  print(i)   print(j) Here is output This is ApplyBigAnalytics  Hello world  2  1 Related Posts The Real Purpose of Underscore in Python 
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