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Lambda, Kinesis, DynamoDB - data streaming and persistence

Spark/Big Datahard0.5 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-25

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Lambda, Kinesis, DynamoDB: Kinesis—ingest streaming data (shards, retention). Lambda—serverless compute, process events. DynamoDB—NoSQL...

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Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
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1,863
across 7 categories
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Key Concepts Tested
optimizationpartitionsql
Expert Answer
92 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Lambda, Kinesis, DynamoDB: Kinesis—ingest streaming data (shards, retention). Lambda—serverless compute, process events. DynamoDB—NoSQL storage, low latency. Pipeline: Kinesis → Lambda (trigger) → transform → DynamoDB (put). Use for: real-time dashboards, event processing. Best practice: Size Kinesis shards; use Lambda batches; design DynamoDB keys for access patterns; use DynamoDB Streams for change data.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

⚡
Pro Tip

Red Flag: Shard sizing guess. Pro-Move: 'Size shards; Lambda batches; DynamoDB keys.'

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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