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Home/Questions/Cloud/Tools/Handling Large-Scale Data Ingestion in AWS Pipelines

Handling Large-Scale Data Ingestion in AWS Pipelines

Cloud/Toolshard0.2 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

Architectural options: (1) Kinesis Streams/Firehose—streaming, auto-scale, batch to S3. (2) Glue—batch, scale DPUs. (3) DMS—DB migration, CDC. (4) S3 multipart—large files. (5) Shard/partition for parallelism. Scalability: Firehose for throughput; Glue for batch....

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Frequency
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Asked at 1 company
Category
179
questions in Cloud/Tools
Difficulty Split
104E|27M|48H
in this category
Total Bank
1,863
across 7 categories
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Key Concepts Tested
partition
Expert Answer
45 words

Architectural options: (1) Kinesis Streams/Firehose—streaming, auto-scale, batch to S3. (2) Glue—batch, scale DPUs. (3) DMS—DB migration, CDC. (4) S3 multipart—large files. (5) Shard/partition for parallelism. Scalability: Firehose for throughput; Glue for batch. Best practice: Firehose for streams; batch to larger files; partition; backpressure and retries.

⚡
Pro Tip

Red Flag: No backpressure—downstream overwhelmed. Pro-Move: 'Firehose 50K/sec; we batch 128MB to S3; partition by hour—sustained 2TB/day.'

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