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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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Text Vs. Binary Vs. UTF-8 Top differences

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Here are the differences between Text files, Binary files, and UTF-8. These would help understanding files correctly for beginners. Text File It contains plain text characters. When you open a text file in a text editor, it displays human-readable content.  The text may not be in a language you know or understand, but you will see mostly normal characters that you can type at any keyboard. Binary File It stores information in bytes that aren’t quite so human readable.  If you open the binary file in a text editor, it will not be readable. UTF-8 UTF-8 is short for Unicode Transformation Format, 8-bit, and is a standardized way to represent letters and numbers on computers. The original ASCII set of characters, which contains mostly uppercase and lowercase letters, numbers, and punctuation marks, worked okay in the early days of computing. But when other languages were brought into the mix, these characters were just not enough. Many standards for dealing with other languages ha...
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Python Web data - How to Extract HTML Tags Easily

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With BeautifulSoup you can extract HTML and XML tags easily that present in Web data. Here is the best example of how to remove these. The prime step of text analytics is cleaning . You can remove HTML tags using BeautifulSoup parser. Check out Python Logic and removing HTML tags. When analyzing web data, consider the below examples for your projects. Python Ideas to Remove HTML tags How I Removed Using BeautifulSoup Import BeautifulSoup Python Logic to Remove HTML tags Before and after executing the code 1. Import BeautifulSoup import  BeautifulSoup  from  bs4 2. Python BeautifulSoup: How to Remove HTML Tags from  bs4  import  BeautifulSoup soup =  BeautifulSoup ("<!DOCTYPE html><html><body><h1>My First Heading</h1><p>My first paragraph.</p></body></html>") text = soup. get_text() print (text) 3. Before and After Run Before the run see the below code. Before Executing the code After Run the ...
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