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Data pipelines

Intermediate⏱ 14 min read📅 Updated: 2026-02-17

Introduction

Nee daily morning coffee podra process yosichchu paaru ☕:

Water boil pannunga
Coffee powder add pannunga
Milk add pannunga
Filter pannunga
Cup la pour pannunga

Idhu oru pipeline! One step complete aana, next step start aagum. Data world la idha dhaan Data Pipeline nu solluvanga! 🔄

Data Engineering la pipelines is the backbone. Without pipelines, data manually move pannanum — that's like manually carrying water from river to every house. Not scalable! 😅

What is a Data Pipeline?

Data Pipeline = Automated sequence of steps that moves data from source to destination, transforming it along the way.

Think of it like a factory assembly line 🏭:

Raw materials (raw data) enter
Each station (step) does something
Finished product (clean, usable data) comes out

Key characteristics:

Automated — manual intervention vendaam
Scheduled — daily, hourly, or real-time
Reliable — failures handle pannum
Scalable — data volume increase aanalum work aagum
Monitored — problems detect pannum

Pipeline vs Script:

Feature	Simple Script	Data Pipeline
Error handling	❌ Basic	✅ Retry, alerts
Scheduling	❌ Manual/cron	✅ Orchestrator
Monitoring	❌ Logs only	✅ Dashboard, alerts
Scalability	❌ Single machine	✅ Distributed
Dependencies	❌ None	✅ DAG-based

Pipeline Components

🏗️ Architecture Diagram

┌─────────────────────────────────────────────────┐
│              DATA PIPELINE ANATOMY                │
├─────────────────────────────────────────────────┤
│                                                   │
│  ┌──────────┐   ┌───────────┐   ┌────────────┐  │
│  │  SOURCE   │──▶│ INGESTION │──▶│ PROCESSING │  │
│  │          │   │           │   │            │  │
│  │ APIs     │   │ Extract   │   │ Clean      │  │
│  │ DBs      │   │ Validate  │   │ Transform  │  │
│  │ Files    │   │ Buffer    │   │ Enrich     │  │
│  │ Streams  │   │           │   │ Aggregate  │  │
│  └──────────┘   └───────────┘   └─────┬──────┘  │
│                                       │          │
│  ┌──────────┐   ┌───────────┐   ┌─────▼──────┐  │
│  │MONITORING│◀──│   LOAD    │◀──│  STORAGE   │  │
│  │          │   │           │   │            │  │
│  │ Alerts   │   │ Warehouse │   │ Data Lake  │  │
│  │ Metrics  │   │ Dashboard │   │ Cache      │  │
│  │ Logs     │   │ API       │   │ Feature DB │  │
│  └──────────┘   └───────────┘   └────────────┘  │
└─────────────────────────────────────────────────┘

Types of Data Pipelines

Data pipelines different types la varum:

1. Batch Pipeline 📦

Data ah chunks la process pannum
Schedule: hourly, daily, weekly
Example: Daily sales report generation
Tools: Airflow + Spark

2. Streaming Pipeline 🌊

Data ah real-time la process pannum
Continuous flow — no waiting
Example: Fraud detection, live dashboard
Tools: Kafka + Flink

3. Micro-batch Pipeline ⚡

Batch + Streaming hybrid
Small batches, frequent intervals (seconds/minutes)
Example: Near real-time analytics
Tools: Spark Streaming

4. Event-driven Pipeline 🎯

Triggered by events (new file uploaded, API call)
No fixed schedule
Example: Image upload → AI processing → store results
Tools: AWS Lambda, Cloud Functions

Type	Latency	Complexity	Cost	Use Case
Batch	Minutes-Hours	Low	💰	Reports, ETL
Streaming	Milliseconds	High	💰💰💰	Fraud, live
Micro-batch	Seconds	Medium	💰💰	Near real-time
Event-driven	Seconds	Medium	💰	Triggers

DAG — The Heart of Pipelines

Pipeline steps ku order irukku — some steps parallel ah run aagalaam, some dependent. Idha represent panra structure ku DAG (Directed Acyclic Graph) nu per.

Directed = Direction irukku (A → B, not B → A)

Acyclic = No cycles (A → B → C → A ❌ not allowed)

Graph = Nodes and edges

Example DAG:

code

Extract_Users ──┐
                ├──▶ Join_Data ──▶ Load_Warehouse
Extract_Orders ─┘         │
                          ▼
                    Generate_Report

Here, Extract_Users and Extract_Orders parallel ah run aagum. Join_Data rendu complete aana apram dhaan start aagum. Idhu dhaan DAG power! 💪

Airflow popular orchestrator — DAGs define panna Python use pannum. Every production pipeline oru DAG dhaan!

Building Your First Pipeline

Simple pipeline build pannalaam — step by step:

Scenario: E-commerce sales data daily process pannanum

Step 1: Extract 📥

python

# API la irundhu data fetch
sales_data = fetch_api("/api/daily-sales")

Step 2: Validate ✅

python

# Data quality check
assert len(sales_data) > 0, "No data!"
assert all(r['amount'] > 0 for r in sales_data)

Step 3: Transform 🔄

python

# Clean and enrich
for record in sales_data:
    record['date'] = parse_date(record['timestamp'])
    record['category'] = lookup_category(record['product_id'])
    record['revenue'] = record['amount'] * record['price']

Step 4: Load 📤

python

# Database la insert
db.bulk_insert('sales_summary', transformed_data)

Step 5: Notify 📧

python

# Success notification
send_slack("✅ Daily sales pipeline complete!")

Ivlo dhaan basic pipeline! Idha Airflow la schedule pannina — automated data pipeline ready! 🎉

Error Handling Best Practices

💡 Tip

Production pipelines la error handling critical:

💡 Retry Logic — Transient errors ku automatic retry pannunga (3 times with exponential backoff)

💡 Idempotency — Same pipeline 2 times run aanalum same result varanam. Duplicate data create aaga koodadhu!

💡 Dead Letter Queue — Process panna mudiyaadha records ah separate ah store pannunga. Later fix pannalam.

💡 Alerting — Pipeline fail aana Slack/email alert varanam. Silent failures are the worst!

💡 Checkpointing — Long pipeline la middle la fail aana, beginning la irundhu start panna vendaam. Save progress at each step!

💡 Data Validation — Input AND output validate pannunga. Bad data downstream ku pogakoodadhu.

Pipeline Tools Comparison

Top pipeline tools — which one for what?

Tool	Type	Best For	Learning Curve
Apache Airflow	Orchestration	Batch pipelines	🟡 Medium
Prefect	Orchestration	Modern Python pipelines	🟢 Easy
Dagster	Orchestration	Data-aware pipelines	🟡 Medium
Apache Kafka	Streaming	Real-time data flow	🔴 Hard
Apache Spark	Processing	Large-scale batch	🔴 Hard
dbt	Transformation	SQL-based transforms	🟢 Easy
Luigi	Orchestration	Simple batch	🟢 Easy
AWS Step Functions	Orchestration	Serverless pipelines	🟡 Medium

Beginner recommendation: Airflow + dbt combo start pannunga. Industry standard, jobs la demand irukku! 🎯

Common Pipeline Patterns

Real-world la common patterns:

1. Fan-Out Pattern 🌟

One source → multiple destinations

code

Raw Data ──▶ Warehouse
         ──▶ ML Feature Store
         ──▶ Real-time Dashboard

2. Fan-In Pattern 🔄

Multiple sources → one destination

code

MySQL DB ────┐
Salesforce ──┼──▶ Central Data Lake
CSV Files ───┘

3. Lambda Architecture λ

Batch + Streaming parallel ah run aagum

Batch layer: Complete, accurate (slow)
Speed layer: Real-time, approximate (fast)
Serving layer: Both merge pannudhu

4. Medallion Architecture 🏅

Data quality stages:

Bronze — Raw data (as-is from source)
Silver — Cleaned, validated data
Gold — Business-ready, aggregated data

Databricks popularize pannina pattern — very common in modern data platforms!

Pipeline Monitoring — Ignore Pannaadheenga!

⚠️ Warning

⚠️ Silent pipeline failures are the WORST thing in data engineering!

Imagine: Pipeline silently fail aairuchu, but dashboard still shows old data. Management old data vachu decisions edukuraanga. Disaster! 💥

Must-have monitors:

- ⏱️ Latency — Pipeline expected time la complete aagudha?

- 📊 Data volume — Expected row count varudha? Sudden drop = problem

- ✅ Data quality — Null values percentage spike aana alert

- 🔴 Failure rate — How many runs fail per week?

- 💾 Resource usage — Memory, CPU, disk space

Tools: Datadog, Grafana, PagerDuty, or even simple Slack webhooks

Golden rule: If you can't monitor it, don't deploy it! 🏆

Real-World Pipeline: Netflix Recommendation

✅ Example

Netflix oda recommendation pipeline paapom 🎬:

Batch Pipeline (Daily):

1. 📥 User watch history collect (billions of events)

2. 🧹 Clean — incomplete sessions remove

3. 🔄 Transform — user preferences calculate

4. 🤖 ML model retrain with new data

5. 📤 Recommendations update for all 200M+ users

6. 💾 Cache results in CDN

Streaming Pipeline (Real-time):

1. 📱 User clicks "play" on a movie

2. ⚡ Event stream to Kafka

3. 🧮 Real-time feature update

4. 🎯 Immediate recommendation adjustment

5. 📺 "Because you watched..." section updates

Scale: Netflix processes 1+ trillion events/day through their pipelines! They use custom tools but concepts are the same. 🤯

Testing Your Pipelines

Pipeline testing neglect pannaadheenga! Production la break aanalaa big trouble:

1. Unit Tests 🧪

Individual transformation functions test pannunga
Input → Expected output verify pannunga

2. Integration Tests 🔗

Full pipeline end-to-end test with sample data
Source to destination flow correct ah work aagudha?

3. Data Quality Tests ✅

Schema validation — columns correct ah irukka?
Null check — unexpected nulls irukka?
Range check — values expected range la irukka?
Uniqueness — duplicate records irukka?

4. Performance Tests ⚡

Large dataset la pipeline timeout aagudha?
Memory limits exceed aagudha?

Tools: Great Expectations, dbt tests, pytest — ivanga use pannunga. "Untested pipeline = ticking time bomb" 💣

Prompt: Design a Pipeline

📋 Copy-Paste Prompt

You are a senior data engineer. Design a data pipeline for this scenario:

An e-commerce company wants to:
1. Collect product reviews from their website (1M reviews/day)
2. Run sentiment analysis using an AI model
3. Aggregate sentiment scores per product
4. Update a dashboard in real-time
5. Send alerts when negative sentiment spikes

Provide:
- Architecture diagram (text-based)
- Tool choices with reasoning
- Error handling strategy
- Estimated processing time
- Cost optimization tips

Use Tanglish for explanation.

Pipeline Best Practices Summary

Years of experience la irundhu vandra best practices:

✅ Idempotent design — Re-run safe ah irukanum

✅ Incremental processing — Full reload avoid pannunga, only new data process pannunga

✅ Schema evolution — Source schema maari na pipeline break aagakoodadhu

✅ Logging — Every step log pannunga, debugging ku essential

✅ Documentation — Pipeline purpose, owner, schedule document pannunga

✅ Version control — Pipeline code Git la irukanum

✅ Environment separation — Dev, staging, production separate ah irukanum

✅ Cost monitoring — Cloud bill track pannunga monthly

Next article: Data Lakes vs Warehouses — data enga store pannanum nu paapom! 🎯

✅ Key Takeaways

✅ Data Pipeline — Automated sequence of steps data extract, transform, load pannum. Manual scripts la irundhu major difference — pipelines reliable, schedulable, monitorable, scalable

✅ DAG (Directed Acyclic Graph) — Pipeline oda structure representing tasks and dependencies. Acyclic means circular dependencies illegal. Enables parallel execution of independent tasks

✅ Batch vs Streaming — Batch: minutes-hours latency, lower complexity. Streaming: milliseconds-seconds latency, higher complexity. Most use cases batch enough; real-time specific scenarios only needed

✅ Error Handling Essential — Retry logic, idempotent design, dead letter queues, checkpointing necessary. Silent failures worst-case; monitoring setup mandatory

✅ Pipeline Components — Source → Ingestion → Processing → Storage → Load → Monitoring. Each stage failures handle panna strategies venum

✅ Orchestration Tools — Airflow industry-standard Python ecosystem la. Prefect (modern Python). Dagster (data-aware). Luigi (simple). Each different trade-offs

✅ Monitoring Critical — Pipeline latency, data volume, quality checks, failure rates track pannum. If you can't monitor it, don't deploy it philosophy follow pannunga

✅ Testing Pipelines — Unit tests (individual functions), Integration tests (end-to-end), Data quality tests (schema, nulls, ranges), Performance tests required

🏁 🎮 Mini Challenge

Challenge: Airflow DAG ezhudhu

Simple pipeline orchestrator setup pannunga:

Step 1 (Setup - 5 min):

bash

pip install apache-airflow
airflow db init

Step 2 (DAG create - 15 min):

python

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

def extract():
    print("Extracting data...")
    return {'rows': 1000}

def transform(**context):
    data = context['ti'].xcom_pull(task_ids='extract_task')
    print(f"Transforming {data['rows']} rows...")
    return {'processed': data['rows']}

def load(**context):
    data = context['ti'].xcom_pull(task_ids='transform_task')
    print(f"Loading {data['processed']} rows...")

with DAG('simple_etl', start_date=datetime(2026, 1, 1), schedule='@daily') as dag:
    extract_task = PythonOperator(task_id='extract_task', python_callable=extract)
    transform_task = PythonOperator(task_id='transform_task', python_callable=transform)
    load_task = PythonOperator(task_id='load_task', python_callable=load)

    extract_task >> transform_task >> load_task

Step 3 (Run - 5 min):

bash

airflow scheduler &  # Terminal 1
airflow webserver    # Terminal 2 – http://localhost:8080

Result: Web UI la DAG visualization, execution tracking, logs – production-ready! 🚀

💼 Interview Questions

Q1: Data pipeline na enna? Script la irundhu difference?

A: Script = manual run pannum. Pipeline = automated, scheduled, monitored. Pipeline retries handle pannum, failures alert pannum, dependencies track pannum, logs maintain pannum. Production systems pipelines venum – script unreliable!

Q2: DAG (Directed Acyclic Graph) na enna? Why important?

A: DAG = pipeline structure (tasks + dependencies). Directed = order irukku (A→B). Acyclic = cycles illa (A→B→A invalid). Airflow DAG la think pannu – tasks parallel ah run aagalaam, dependencies handle aagum. Idhu distributed execution enable pannum!

Q3: Failure scenario – pipeline fail aana task recover aaganum? Strategy?

A: Retry logic implement (exponential backoff). Idempotency ensure (re-run safe). Checkpointing (progress save). Dead letter queue (process aaga mudiyaadha records). Alerting (Slack/email). Human intervention manual – oru fois debug pannum, then auto-retry. Planning critical!

Q4: XCom (Cross-Communication) – why complex?

A: XCom metadata DB store (small data only). Large data pass panna performance suffer. Solution: S3 paths pass pannu, actual data illa. Or structured approach – features feature store la, models model registry la. XCom lightweight keep pannunga!

Q5: Airflow deployment production – considerations?

A: High availability (3+ scheduler, load balancer). Distributed executor (Celery or Kubernetes). Metadata DB resilient (multi-replica). Monitoring (metrics, logs). Security (RBAC, connections encrypted). Cost (workers scale up-down based on load). AWS Managed Workflows (MWAA) or Kubernetes simpler – self-host complex!

Frequently Asked Questions

❓ Data pipeline na enna?

Data pipeline na automated series of steps — data ah source la irundhu extract panni, transform panni, destination la load panra process. Manual intervention illama run aagum.

❓ ETL vs Data Pipeline — enna difference?

ETL is one type of data pipeline. Data pipeline is a broader term — ETL, ELT, streaming pipelines, real-time pipelines ellam data pipelines dhaan.

❓ Data pipeline build panna enna tools use pannalam?

Apache Airflow (orchestration), Apache Spark (processing), Kafka (streaming), dbt (transformation), Prefect, Dagster — ivanga popular tools.

❓ Data pipeline fail aana enna pannanum?

Retry logic implement pannunga, alerting setup pannunga (Slack/email), dead letter queues use pannunga, and idempotent pipelines design pannunga so re-run safe ah irukkum.

🧠Knowledge Check

Quiz 1 of 1

DAG la "Acyclic" na enna meaning?

0 of 1 answered

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Courses

Learning Paths

Exam Prep

Data pipelines

Introduction

What is a Data Pipeline?

Pipeline Components

Types of Data Pipelines

DAG — The Heart of Pipelines

Building Your First Pipeline

Error Handling Best Practices

Pipeline Tools Comparison

Common Pipeline Patterns

Pipeline Monitoring — Ignore Pannaadheenga!

Real-World Pipeline: Netflix Recommendation

Testing Your Pipelines

Prompt: Design a Pipeline

Pipeline Best Practices Summary

✅ Key Takeaways

🏁 🎮 Mini Challenge

💼 Interview Questions

Frequently Asked Questions