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Stream vs Batch Processing - System Design Interview Guide
Table of Contents
- ? Batch vs Stream Processing (Interview Master Sheet)
- 1?? Core Idea (1-liner difference)
- 2?? How They Work (Architecture Level)
- ?? Batch Processing (Architecture)
- ?? Stream Processing (Architecture)
- 3?? Key Trade-offs (Must Say)
- 4?? Signals / Hints (Interviewer Gold ?)
- ?? Choose Batch Processing if:
- ?? Choose Stream Processing if:
- 5?? Real System Design Decisions
- ?? Batch Design Choices:
- ?? Stream Design Choices:
- 6?? Hybrid Approach (Very Important ??)
- Hybrid Example:
- 7?? FAANG-Level Interview Questions + Answers
- ? Q1: Why not always use stream processing?
- ? Q2: How do you handle failures in stream processing?
- ? Q3: What is Lambda Architecture?
- ? Q4: How to ensure data consistency in streams?
- ? Q5: Batch vs Stream in big companies?
- 8?? Quick Examples (Must Remember)
- 9?? 30-Second Revision (Final Script)
- ?? Final FAANG Tip
? Batch vs Stream Processing (Interview Master Sheet)
1?? Core Idea (1-liner difference)
- Batch Processing ? Process data in large chunks (delayed)
- Stream Processing ? Process data in real-time (continuous)
?? Script:
�Batch processes data in bulk with delay, while stream processes data continuously in real time.�
2?? How They Work (Architecture Level)
?? Batch Processing (Architecture)
Flow:
- Data collected (logs, events)
- Stored (data lake / DB)
- Scheduled job runs (hourly/daily)
- Process ? Output
Components:
- Storage (S3 / HDFS)
- Scheduler (cron, Airflow)
- Processing engine
Tools:
- Apache Hadoop
- Apache Spark
?? Script:
�Batch systems collect data over time and process it periodically using distributed processing frameworks.�
?? Stream Processing (Architecture)
Flow:
- Event generated (click, payment)
- Sent to message queue
- Stream processor consumes instantly
- Output/Action (alert, DB update)
Components:
- Message broker
- Stream processor
- Real-time sink (DB, cache)
Tools:
- Apache Kafka
- Apache Flink
?? Script:
�Stream processing uses event-driven architecture where data is processed immediately as it arrives.�
3?? Key Trade-offs (Must Say)
| Factor | Batch | Stream |
|---|---|---|
| Latency | ? High (minutes�hours) | ? Low (ms�seconds) |
| Throughput | ? Very High | ?? Medium�High |
| Complexity | ? Simple | ? Complex |
| Cost | ? Cheaper | ? Expensive |
| Use Case | Offline analytics | Real-time systems |
?? Script:
�Batch optimizes for throughput and cost, while stream optimizes for latency and real-time insights.�
4?? Signals / Hints (Interviewer Gold ?)
?? Choose Batch Processing if:
- No real-time requirement
- Large historical data
- Reports / analytics
- Cost-sensitive system
?? Examples:
- Payroll
- Daily reports
- Data warehousing
?? Script:
�If latency is not critical and we are dealing with large historical datasets, I will use batch processing.�
?? Choose Stream Processing if:
- Real-time decisions needed
- User-facing features
- Continuous data flow
- Low latency required
?? Examples:
- Fraud detection
- Live dashboards
- Notifications
?? Script:
�If the system requires immediate action or real-time insights, I will use stream processing.�
5?? Real System Design Decisions
?? Batch Design Choices:
- ETL pipelines
- Data lake + warehouse
- Scheduled jobs
?? Example:
- Nightly analytics pipeline
?? Stream Design Choices:
- Event-driven architecture
- Pub/Sub model
- Stateless/stateful processing
?? Example:
- Real-time fraud detection pipeline
6?? Hybrid Approach (Very Important ??)
?? Modern systems use Lambda / Kappa architecture
Hybrid Example:
- Stream ? real-time dashboard
- Batch ? historical accuracy correction
?? Script:
�I would combine both: stream for real-time insights and batch for accurate long-term computation.�
7?? FAANG-Level Interview Questions + Answers
? Q1: Why not always use stream processing?
? Answer:
�Because it is complex, expensive, and harder to maintain. If real-time is not needed, batch is more efficient.�
? Q2: How do you handle failures in stream processing?
? Answer:
- Checkpointing
- Replay from Kafka
- Exactly-once semantics
? Q3: What is Lambda Architecture?
? Answer:
�It combines batch and stream: batch layer for accuracy, speed layer for real-time processing.�
? Q4: How to ensure data consistency in streams?
? Answer:
- Idempotency
- Windowing
- Event ordering
? Q5: Batch vs Stream in big companies?
? Answer:
�Companies use stream for user-facing features and batch for analytics and reporting.�
8?? Quick Examples (Must Remember)
- Batch ? Salary processing
- Stream ? Credit card fraud detection
9?? 30-Second Revision (Final Script)
?? Script:
�Batch processing handles large volumes of data with high throughput but higher latency, making it ideal for offline analytics. Stream processing handles continuous data with low latency, enabling real-time decisions. In practice, I use stream for real-time features and batch for historical accuracy and cost optimization.�
?? Final FAANG Tip
?? Always say:
�The choice depends on latency requirements � real-time vs offline.�