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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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How to Use Arguments in Real Python Programs

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Here are four types of exclusive arguments in python. The arguments are passers to functions. These are Required arguments, Keyword arguments, Default arguments and Variable-length arguments. Here're 4 Important Arguments in Python The arguments supply input to functions . Because these are various types, herein you will know the details and usage of those types. Argument#1: Required arguments Positional arguments are known as  required arguments  and are passed to a function in the correct order. The arguments in the function call should match the number in the function definition. Sample program: def cal(a,b):     """program to calculate sum of 2 numbers"""     sum=a+b     return sum a=int(input("enter 1st number")) b=int(input("enter 2nd number")) print(cal(a,b)) Output enter 1st number 10 enter 2nd number 22 32 ** Process exited - Return Code: 0 ** Press Enter to exit terminal Argument#2: Keyword arguments A default argument is...
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