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Can you explain how streams and tasks handle data freshness in near real-time?

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Cognizant
Key Concepts Tested
spark
Expert AnswerPremium
93 wordsInterview-ready
**Why freshness matters**: SLA defines acceptable latency; architecture determines achievable freshness. **Streams (Kafka, Kinesis)**: Event flow; consumers read at offset. **Processing tasks**: Spark micro-batch (e.g., `trigger(processingTime="1 minute")`); Flink continuous. Freshness = ingestion latency + processing latency + sink latency. **Levers**: Reduce batch interval (trade throughput); increase parallelism; optimize checkpointing; backpressure handling....
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