Lesson · Hinglish · ⏱ 8 min padhne mein

🚰 Data pipelines: ETL, Airflow, dbt aur Spark

Q: Kal ki pipeline aadhe mein fail ho gayi. Idempotent hona kyun zaroori hai?

Bina data duplicate kiye use safely dobara chala sakte ho. Data kaam mein re-runs normal hain, aur idempotency double-counting se bachaati hai.

🏠 Asli zindagi ki misaal

Mumbai ke dabbawale gharon se tiffin uthaate hain, stations pe sort karte hain, aur offices tak pahunchaate hain, schedule pe, roz, lagbhag bina galti ke. Data pipeline data ke liye yahi karti hai: apps se data nikaalo (extract), clean aur transform karo, aur wahan load karo jahan analysts aur models ko chahiye, reliably aur time pe.

💡 Aasaan shabdon mein

ETL / ELT matlab extract (databases, APIs, files se), transform (clean, join, aggregate) aur load (warehouse ya lake mein). Airflow steps ko orchestrate karta hai: DAG tasks, unka order, schedules, retries aur alerts define karta hai. dbt transformations ko warehouse ke andar tested SQL models ke roop mein likhta hai. Spark ek machine ke liye bahut bade data ko cluster mein kaam baant kar process karta hai. Acchi pipelines idempotent hoti hain (dobara chalaana safe), freshness ke liye monitor hoti hain, aur documented hoti hain, kyunki aage ka har AI system unhi pe depend karta hai.

🎬 Dekho kaise kaam karta hai

Mumbai ke dabbawalon jaisa: har raat data sources se uthaaya jaata hai, clean aur join hota hai, phir warehouse tak pahunchaaya jaata hai jise dashboards aur models use karte hain.

🪜 Step by step

Extract: sources (databases, APIs, files) se schedule pe ya badalne pe data nikaalo.
Raw data ko bina badle warehouse ya lake mein load karo, taaki hamesha dobara bana sako.
SQL (dbt) ya Spark se saaf, modelled tables mein transform karo.
Steps ko Airflow (ya waise tool) se orchestrate karo: order, schedule, retries aur alerts.
Har run ko idempotent banao, ek baar mein ek date partition process karke use overwrite karke.

🔑 Zaroori shabd

ETL / ELT: Extract, transform, load: data ko le jaana aur shape dena.
DAG: Dependencies wale tasks ka graph, schedule pe chalta hai (Airflow).
Idempotent: Do baar chalaane pe bhi result ek baar jaisa hi aata hai.

👀 Example dekho

python

from airflow.decorators import dag, task
from datetime import datetime

@dag(schedule="0 2 * * *", start_date=datetime(2026, 1, 1), catchup=False)
def daily_orders():
    @task(retries=3)
    def extract(): ...          # pull yesterday's orders from the app DB
    @task
    def transform(rows): ...    # clean, dedupe, join with customers
    @task
    def load(df): ...           # write to warehouse table fact_orders
    load(transform(extract()))

daily_orders()

Har raat 2 baje retries ke saath chalti hai. Zyaadatar data platforms isi pattern pe chalte hain.

⚠️ Aam galtiyaan jinse bachna hai

Har run pe data append karna, jisse retry pe numbers double ho jaate hain.
Steps ke beech tests nahi, jisse kharab data chupke se dashboards aur models tak pahunch jaata hai.

✅ Jaldi se check karo

Kal ki pipeline aadhe mein fail ho gayi. Idempotent hona kyun zaroori hai?

Bina data duplicate kiye use safely dobara chala sakte ho
Use schedule ki zaroorat nahi
Woh fast chalti hai

Jawaab dekho

A. Bina data duplicate kiye use safely dobara chala sakte ho: Data kaam mein re-runs normal hain, aur idempotency double-counting se bachaati hai.

🙋 Seekhne wale yeh poochhte hain

Spark ya SQL?

Agar warehouse volume sambhaal leta hai to dbt ke saath SQL simple hai. Bahut bade data, complex processing ya unstructured data ke liye Spark.

AI pipelines ke liye kya badalta hai?

Zyaada unstructured data (PDFs, emails), aise steps jo rate limits aur cost ke saath LLMs ya embedding models call karte hain, aur fresh rakhne wale vector indexes.

📌 Yaad rakho

Extract → transform → load: orchestrated, tested, dobara chalne laayak aur monitored.

📖 Is lesson ke shabd (AI dictionary)

Poora course, aapke roadmap ke hisaab se

Progress tracking, quick checks aur step-by-step order ke saath. Yeh lesson in courses ka hissa hai:

Data Engineer (AI focus)

Course mein seekho