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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 Read CSV file Data in Python

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Here is a way to read  CSV files  in Python pandas. The packages you need to import are numpy and pandas. On the flip side, f or Text files, you don't need to import these special libraries since python by default support it. Python pandas read_csv >>> import numpy as np >>> import pandas as pd To see how pandas handle this kind of data, we'll create a small CSV file in the working directory as ch05_01.csv. white, red, blue, green, animal 1,5,2,3,cat  2,7,8,5,dog  3,3,6,7,horse  2,2,8,3,duck  4,4,2,1,mouse Since this file is comma-delimited , you can use the read_csv() function to read its content and convert it to a dataframe object. >>> csvframe = pd.read_csv('ch05_01.csv') >>> csvframe white red blue green animal 0 1 5 2 3 cat 1 2 7 8 5 dog 2 3 3 6 7 horse 3 2 2 8 3 duck 4 4 4 2 1 mouse Python reading text files Sinc...
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