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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 Improved Logic to Calculate Factorial

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I am practicing Python programming. In this post, I have explained logic to calculate the factorial using a function. This  function  you can call it a user-defined function. The name of the function is factorial.py. In real-time, you can write a program in a file and run it in python console . The main task of a developer is to create functions for the reusable code. They call these functions whenever they need. Factorial calculation program for supplied input value.   Factorial Logic in Python. I have completed this logic in 3 steps. Write factorial.py Import Execute it Write Factorial.py  Here you need to define a function. Use 2 for loops, and write your logic. This is done on LInux operating system. You can also try on Linux. After, ESC command Use, :wq to come out of the module. Import Factorial.py Go to Python console, using 'python' command. Use import factorial.py command. Execute Factorial.py  >>...
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