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Intermediate Topic 34 of 51 Distributed Systems & Cloud

Batch vs Stream Processing

Processing stored data versus continuous flows.

How it works

Batch processing handles a bounded, finite dataset collected over time, running jobs periodically such as nightly, optimizing for throughput and accuracy with tools such as Hadoop and Spark batch. Stream processing handles unbounded, continuously arriving events with low latency, using windowing, event-time semantics, watermarks, state and exactly-once guarantees, with engines such as Flink, Kafka Streams and Spark Structured Streaming. Architectures such as Lambda combine both, while Kappa treats everything as streams over a replayable log.

Where it is used

Billing, reports and model training suit batch; fraud detection, monitoring, real-time recommendations, IoT analytics and alerting need streaming.

Common pitfall

Streaming adds complexity: late and out-of-order events, state management, backpressure, reprocessing and exactly-once semantics. Batch has latency that may be unacceptable. Maintaining two code paths in Lambda architecture causes inconsistency. Use event time rather than processing time, define windows and lateness policies deliberately and choose based on latency needs.

Where it fits in the course

Batch vs Stream Processing is topic 34 of 51 in Distributed Systems & Cloud, rated Intermediate. It follows MapReduce. The next topic is Apache Kafka.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Distributed Systems & Cloud quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Batch vs Stream Processing?
Processing stored data versus continuous flows. It belongs to Distributed Systems & Cloud and is an intermediate-level topic.
Where do people use Batch vs Stream Processing?
Billing, reports and model training suit batch; fraud detection, monitoring, real-time recommendations, IoT analytics and alerting need streaming.
What is the most common mistake with Batch vs Stream Processing?
Streaming adds complexity: late and out-of-order events, state management, backpressure, reprocessing and exactly-once semantics.
What should I learn before this topic?
Start with MapReduce, the topic just before it in Distributed Systems & Cloud.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Distributed Systems & Cloud quiz.
Is Batch vs Stream Processing hard to learn?
It is an Intermediate topic. Be comfortable with the Beginner topics in this area first, then read the common pitfall carefully, since it is where most mistakes happen.
How long does it take to learn Batch vs Stream Processing?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Batch vs Stream Processing?
MapReduce, Apache Kafka, Distributed File Systems are closely connected. The full list is in the related topics section of this page.
What should I study after Batch vs Stream Processing?
Continue with Apache Kafka, the next topic in Distributed Systems & Cloud.

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