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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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6 Python Directory Commands Useful to Read

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Here's logic to create Python Directory . You need to import the 'OS' package to create Python directory. Here're the list of Python directory commands. Python How to Create Directory Here's Logic You can create and delete directories in Python. Here's the helpful logic you can use for your projects. For this, you need to import 'os'.  Python Directories 1. How to Create a Directory Import os  os.mkdir('datafiles') 2. How to Change Directory os.chdir('newdirectory') 3. How to Create a Directory and then Create a file in it import os  os.mkdir('datafiles/newfiles');  os.chdir('datafiles/newfiles');  fp=open('input.txt', 'w')  fp.write('Hello, Welcome to Programming in Python')  fp.close() 4. How to Know Present Working Directory os.getcwd() 5. How to delete a directory os.rmdir('directoryname') 6. How to get the List of Directories os.listdir() Keep Reading You May Also Like:   55 Best Gift I...
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