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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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Python Function Argument: How to Pass it to Decorator

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A decorator is a wrapper and provides additional functionality to a function. Also, it may modify the behavior, such as changing the return type/adding new abilities. Python Decorators Precisely, it is another form of function pointers . Also, it accepts function argument, then either wraps the function or returns a new one. Moreover, it modifies the inputs/outputs supplied to it. It helps you add behavior to functions (objects) dynamically (without changing the function behavior). Function Argument Below, you will find an example of passing a function argument to a decorator. The below function modifies inputs and returns output. def to_upper(func):     text=func()     if isinstance(text,str):         return text.upper() def say():     return "welcome" def hello():     return "hello"      a = to_upper(say) print(a)  b = to_upper(hello) print(b)   Output WELCOME HELLO ** Process exited - Return Cod...
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