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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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3 Exclusive Access Modifiers in Python

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Here are three access modifiers in Python - Public, Protect, and Private. Access modifiers control the access to a variable/or method.  You may have a question that does python supports access modifiers? The answer is yes. In general, all the variables/or methods are public. Which means accessible to other classes. The private and protect access modifiers will have some rules. And the notation for protect and private are different. The single underscore is for protected and the double underscore is for private. Here is how to find Python list frequent items. Differences between Public, Protect and Private Public access modifier Public variables are accessible outside the class. So in the output, the variables are displayed. class My_employee:     def __init__(self, my_name, my_age):         self.my_name = my_name  #public         self.my_age = my_age   # public my_emp = My_employee('Raj',34) print(my_emp.my_name) prin...
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