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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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How to write Regular expression Quickly in python and Examples

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Regular Expressions purpose is to find matching string in another string. You will get either 'True' or 'False' as a response. I am not sharing here how to play tennis. My intention is if you just follow ideas, you can play tennis today.

Python supports regular expressions. It has a special library to work with these. I have shared best examples for your quick reference.
 

Python Regular Expressions

  1. What is a regular expression
  2. How does python support
  3. Best examples


1. What is regular expression


>>> haystack = 'My phone number is 213-867-5309.' 
>>> '213-867-5309' in haystack
True


This is just a fundamental use of the regular expression. The real use of Regular Expression comes here. That is - to find if the main has any valid phone number.


Regular expressions also called regexes.

2. Why do we need regx

  1. Data mining - to get required data if it is present are not
  2. Data validations - to get an answer if the received string is valid or not.

Python support


Python has its own regular expression library. That is called re. What you need to do is just import it.

>>>import re


When data matches and not matches

  1. If a match found, it returns the String
  2. If there is no match, it will return null


Example for regex


>>> import re
>>> re.search(r'fox', 'The quick brown fox jumped...')
<_sre.SRE_Match object; span=(16, 19), match='fox'>

Notes: The returned string is 'fox'.


Matching string


>>> match = re.search(r'fox', 'The quick brown fox jumped...')
>>> match.group() 'fox'

Notes: The returned string is 'fox'.



Multiple matches

>>> import re >>> re.findall(r'o', 'The quick brown fox jumped...')
['o', 'o']

Notes: It returns multiple strings.

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