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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 Function: How to Write Error Logic

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Here's an example python user-defined function. Here, you'll know how to write error conditions. The best examples is if and else. I have shared the logic here for your reference. Python is one of the top programming languages. Python does support Functions . While writing functions you also need to consider some errors. Those are called exceptions. I am explaining how to give error logic in a function of Python. Exception-handling means to have logic for error scenarios. Writing exception I will show in a simple Function. In this post I will give you syntax for writing function and exception.  Here is my simple Function that multiplies two numbers. Logic to write Function in Python to Multiply Two Numbers  def product (num1,num2): prod=num1*num2 print( ' The product of the numbers is \t: '+str(prod) Parameters used the Python Function The 'def' means you are defining function with a name of 'product'. In the parameters of 'product', you can s...
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