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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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All About Init and Delete Constructors Python

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Python class has two constructors. One is the init, and the other one is del. Why do you need these two and their real purpose explained? The initialization method is called __init__ while the finalization or destructor method is called __del__. Python methods with a double underscore character are for internal (not intended for direct access by the outside world) use. There are no true private methods in Python classes, but convention says that a method that begins with a single underscore is considered private, and a double underscore indicates it is internal (only to be used by the system.) Python Constructors  Init Constructor object.__init__(self[, ...]) Called after the instance has been created (by  __new__() ), but before it is returned to the caller. The arguments are those passed to the class constructor expression.  If a base class has an  __init__()  method, the derived class’s  __init__()  method, if any, must explicitly call it to en...
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