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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 Categorise Python Top Uses

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Python language is popular in many areas. Below are the real uses of Python. Surely, many developers confuse to tell all the applications. So I have listed for quick reference. 8 Python Real Application Uses #1. Python Application use -  Python Scripts  Very much popular in scripting. You can write scripts in many areas. Like Interfaces, to access files, to access database, and to access the application programs. #2. Python Application use -  Python in Web Development  You can use Python in Web development. There are powerful Python frameworks present such as Django, Flask, Web2Py, and Pyramid. You can develop an application in IoS/Android/Windows. #3. Python Application use -  Graphical User Interface You can develop applications, which need access to GUI. The libraries and APIs like Tkinter, PyQt, PyGTK, and wxPython allow developers to develop GUI-based apps with simple or complex interfaces.  #4. Python Application use -  System Programm...
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