Batch vs real-time

Oru scenario imagine pannunga — nee bank la irukka. 🏦

Scenario 1: Month end la bank un account oda full statement generate pannudhu. All transactions oru batch la process. → Batch Processing

Scenario 2: Nee suspicious transaction pannaa, INSTANTLY un phone la alert varudhu. → Real-time Processing

Both important! But different purposes ku different approaches. Indha article la batch vs real-time processing clear ah purinjidam — with AI context! ⚡

Batch Processing 📦

• Data accumulate aagudhu over time
• Scheduled time la ALL data oru batch la process
• Example: Daily report generation at midnight
• Think: Washing machine — clothes collect panni, once run pannudhu

Real-Time (Stream) Processing ⚡

• Data arrive aagum bodhe process aagudhu
• Continuous, event-by-event processing
• Example: Fraud alert within milliseconds of transaction
• Think: Running tap water — continuous flow, instant use

Key Difference:

• Batch = Wait, collect, process together
• Real-time = Process immediately as it comes

Both approaches data engineering la essential. AI systems both use pannum depending on the need! 🎯

Chennai transport analogy! 🚌

🚌 Batch = Government Bus

- Wait pannum — schedule follow pannum (every 30 min)

- Lots of passengers collect aagum, then oru trip la ellaarum pogum

- Cost effective — per person cheap

- But delay irukku — 30 min wait pannanum

- Predictable, reliable, handles large volumes

🛺 Real-time = Auto Rickshaw

- Instantly available — no waiting

- One passenger, one trip — immediate departure

- Expensive per trip

- But super fast — no delay!

- On-demand, flexible, low latency

🚗 Micro-batch = Uber Pool

- Small groups collect aagum (2-3 passengers)

- Short wait (2-5 min), then go

- Balance between cost and speed

- Near real-time approach!

Most companies use ALL THREE depending on the use case. 💡

Full comparison table:

Rule of thumb:

• Need it NOW? → Real-time ⚡
• Need it TODAY? → Batch 📦
• Need it in a FEW SECONDS? → Micro-batch 🔄

AI systems la both approaches eppadi use aagudhu:

Batch in AI 📦

• Model Training: Historical data la model train → always batch
• Feature computation: Aggregate features (avg spend last 30 days) → batch
• Model evaluation: Test accuracy on large datasets → batch
• Data labeling: Bulk annotation of training data → batch
• Example: Netflix — nightly batch job la recommendation model retrain

Real-Time in AI ⚡

• Model Inference: User request ku instant prediction → real-time
• Feature computation: Current session features (pages viewed NOW) → real-time
• Anomaly detection: Detect fraud AS it happens → real-time
• Personalization: Dynamic content based on live behavior → real-time
• Example: Google Search — real-time ranking as you type

Best Practice: Batch training + Real-time serving = Most common AI pattern!

Train model with batch data, serve predictions in real-time. 🏆

Practical use cases:

Pure Batch 📦

• 📊 Monthly business reports
• 🧠 ML model retraining (weekly/daily)
• 📧 Sending daily digest emails
• 💰 Payroll processing
• 📈 Data warehouse refresh

Pure Real-Time ⚡

• 🚨 Fraud detection (banking)
• 🏥 Patient vital monitoring
• 📍 Live location tracking (Uber/Ola)
• 💬 Chat message delivery
• 🎮 Online gaming state sync

Hybrid (Both) 🔄

• 🛒 E-commerce recommendations (batch train + real-time serve)
• 📱 Social media feed (batch ranking + real-time new posts)
• 🔍 Search engines (batch index + real-time query)
• 📺 Video streaming (batch encoding + real-time adaptive bitrate)
• 🏦 Banking (batch reports + real-time alerts)

Each approach oda challenges:

⚠️ Batch Challenges

- Data staleness — results always "old" by hours

- Large resource spike during batch window

- If batch fails, entire day's data stuck

- Not suitable for time-sensitive decisions

⚠️ Real-Time Challenges

- Complex to build, debug, and maintain

- Expensive — always-on compute resources

- Ordering guarantees hard (which event came first?)

- State management nightmare (what if system crashes mid-stream?)

- Exactly-once processing very difficult

⚠️ Hybrid Challenges

- Double the infrastructure, double the cost

- Keeping batch and real-time results consistent is HARD

- More moving parts = more failure points

- Team needs skills in both paradigms

💡 Pro tip: Start with batch. Add real-time ONLY when business TRULY needs it. Don't over-engineer! 🎯

Batch and Real-time tools:

Beginner path:

1. Learn batch first: Pandas → Spark → Airflow
2. Then real-time: Kafka → Flink basics
3. Advanced: Lambda/Kappa architecture 🎯

Both approaches practically try pannunga:

Batch Exercise 📦 (30 min)

1. Download a large CSV (>100K rows)
2. Write Python script to process in one batch
3. Measure: Time taken, memory used
4. Schedule with cron: 0 2 * * * python batch_etl.py

Real-Time Exercise ⚡ (1 hour)

1. Install Kafka locally (Docker makes it easy)
2. Create a producer that sends events every second
3. Create a consumer that processes each event
4. Observe: Events processed immediately as they arrive!

Compare 🔍

• Process same 100K records: batch (one shot) vs streaming (one by one)
• Notice: Batch faster for bulk, streaming faster for each individual record
• This hands-on experience teaches more than 10 articles! 💪

Mini Project: Stock price alerting system

• Batch: Calculate daily averages
• Real-time: Alert if price drops > 5% in a minute

Summary:

✅ Batch = Collect and process together (bus analogy)

✅ Real-time = Process immediately (auto rickshaw analogy)

✅ Micro-batch = Small frequent batches (Uber Pool analogy)

✅ AI pattern = Batch training + Real-time serving

✅ Start with batch — add real-time only when needed

✅ Lambda Architecture = Batch + Real-time merged

✅ Most modern systems use hybrid approach

Next article: "Data Pipelines Deep Dive" — automated data flow systems build panra art paapom! 🎯

Challenge: Build Both Batch and Real-Time Processing

Compare pannu hands-on – batch vs streaming:

Batch Setup (30 min):

1. CSV file 10,000 rows (stock prices historical data)
2. Pandas script ezhudhu:

1. Time measure pannu

Real-Time Setup (30 min):

1. Docker la Kafka local setup (docker-compose use)
2. Producer: every second stock price send pannu

1. Consumer: real-time price process pannu
2. Latency measure pannu

Compare:

• Batch: 100K records 30 seconds (3,300 recs/sec)
• Real-time: Per-record milliseconds (instant)
• Batch cheaper, real-time responsive – trade-off clear aaidum!

Learning: Both valuable – use case decide pannum! 💡

Q1: Batch vs real-time – use cases sona, practical difference enna?

A: Batch: Collections of events process oru time (hourly, daily). Real-time: Events process as they arrive. Example: banking transactions – daily settlement batch, but fraud detection real-time. Different latency requirements, different architectures, different costs!

Q2: Batch system la hourly batches 2 hours edukudhu, but pipeline fail aana data loss risk?

A: Yes! Checkpoint/restart logic venum. Last successful offset track pannu. Fail aana, last checkpoint la irundhu restart pannu. Idempotency ensure pannu – same batch twice run aanalum duplicate records varakoodadhu. Real-time la more complex!

Q3: Real-time system complex irukku, naan batch dhaan use panna choice. Disadvantage?

A: Stale data. Batch complete aagumbodhu decision too late. Example: Fraud transaction detected 3 hours later – money already transferred! Critical use cases ku real-time needed. But batch simpler, cheaper – 90% cases batch podhum!

Q4: Streaming data ever-increasing – how to handle volume?

A: Consumer group parallel processing (Kafka like). Multiple workers independently consume. Backpressure handle (buffer, sample, drop, scale). Monitoring consumer lag (bottleneck identify). Sometimes micro-batch (frequent small batches) better compromise than true streaming!

Q5: Lambda Architecture (batch + streaming) – implement panna complex?

A: Very complex! Two separate systems maintain pannanum (duplicate logic). Merging batch and stream results tricky (timestamp skew, late arrivals). Modern trend: Kappa (streaming only) or Unified engines (Spark, Flink) support both natively. Over-engineer avoid pannunga – start batch, add real-time if truly needed!