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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 Abstract Classes to Learn Quickly

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Here's the best example of how to understand the abstract classes of python quickly. An abstract class means it is a skeleton. You cannot instantiate abstract classes. For Example: The abstract class name is 'dog' o = dog() It gives an error. Since the ' dog' is an abstract class. I am sharing with you a clear understanding of abstract class. So that you can tell confidently about these classes. The syntax for Abstract Class from abc , import ABC , abstractmethod  class <class name>(ABC): @abstractmethod  def <method name>():  #abstract class definition Python Abstract Class Explanation from abc import ABC, abstractmethod class AbsBaseClass(ABC): def __init__(self): print("Abstract class") @abstractmethod def abMeth(self): pass def conMeth(self): print("I'm a concrete method") class Derived(AbsBaseClass): def abMeth(self): print("I'm redefined") o=Derived() o.abMeth() o.conMeth() References Star Python Certification ...
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