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beginner Phase 1 · Data Engineering Foundations

Batch vs Streaming

Compare batch processing and stream processing approaches including trade-offs in latency, complexity, and cost.

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Batch vs Streaming Processing

Batch vs Streaming Processing

Batch Processing

Process bounded data in chunks. Minutes to hours latency. Higher throughput, lower cost. Tools: Spark, dbt, Airflow. Use for nightly reports, historical analysis, ML training.

Stream Processing

Process unbounded data one record at a time. Milliseconds-seconds latency. Lower throughput, higher cost. Tools: Kafka, Flink, Spark Streaming. Use for real-time alerts, live dashboards.

Lambda Architecture

Combine both: Batch Layer (accuracy) + Speed Layer (low latency) + Serving Layer (merged views). Most production systems use both batch and streaming.

Code Example

Batch: Nightly revenue report

SELECT
DATE_TRUNC('day', order_date) AS day,
SUM(amount) AS revenue
FROM orders
WHERE order_date = CURRENT_DATE - 1
GROUP BY 1;

Streaming: Real-time dashboard (Flink SQL)

SELECT
window_start,
window_end,
SUM(amount) AS current_hour_revenue
FROM TABLE(
TUMBLE(TABLE orders, DESCRIPTOR(order_time), INTERVAL '1' HOUR)
)
GROUP BY window_start, window_end;

Best Practices

  • Start with batch, add streaming only when needed
  • Consider Lambda or Kappa architecture
  • Handle late-arriving data in streaming
  • Monitor consumer lag in streaming systems

Interview Tips

  • Compare batch vs streaming trade-offs
  • Design a Lambda architecture
  • How do you handle late data?

Practice Problems

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Apply Batch vs Streaming

Design and implement a solution that demonstrates understanding of batch vs streaming in a data engineering context. Consider edge cases and performance.

Batch vs Streaming at Scale

Your implementation needs to handle 10x the current data volume. Identify bottlenecks and propose solutions.

Quiz

1. What is the primary benefit of batch vs streaming?

Question 1 options

2. When would you choose batch vs streaming over alternatives?

Question 2 options

Flashcards

Question

What is Batch vs Streaming?

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Answer

Compare batch processing and stream processing approaches including trade-offs in latency, complexity, and cost. Key for Data Engineering Foundations.

Question

When to use Batch vs Streaming?

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Answer

Use when requirements match its strengths. Consider trade-offs vs alternatives.

Revision Notes

Key Takeaways

  • 1. Compare batch processing and stream processing approaches including trade-offs in latency, complexity, and cost.
  • 2. Master batch vs streaming for Data Engineering Foundations
  • 3. Practice with hands-on projects
  • 4. Understand trade-offs and alternatives

Interview Tips

  • • Explain batch vs streaming with real examples
  • • Discuss trade-offs and alternatives
  • • Show how this connects to the broader data stack

Cheat Sheet

Batch vs Streaming — Quick Reference

Description

Compare batch processing and stream processing approaches including trade-offs in latency, complexity, and cost.

Key Points

  • Important concept in Data Engineering Foundations
  • Understanding this is essential for data engineering interviews
  • Practice with real-world scenarios