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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 Work with 'Pointers' in Python

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Pointers denote an address (memory location). It has three identities - Name, Value, and Location (Address). Python doesn't support pointers as-is. You need to import 'ctypes' package to work with C Language. Note : Pointer is popular in C, C++. The called module just uses the value of Pointer (not address).  Below is my detailed post on pointers.   How to work with Pointers To pass a reference(address) to the C interface. You can use C Language in Python by importing 'ctypes.'  Pointer Notation 1. Value 2. Address 3. Name Python Pointers Python doesn't support pointers. C and C++ extensively support pointers. Pointer is nothing but an ADDRESS. It is immutable. That means you can't change the value. Python supports pointers for the purpose to interact with C Language. How to Import 'ctypes' Import 'ctypes' library for the purpose of working with C language.  Here's how to import 'ctypes' for windows and Linux. How to denote Pointe...
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