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Home/Questions/System Design/Architecture/Design a data pipeline to ingest and process data from multiple sources (e.g., S3, Kinesis) to Redshift using Spark.

Design a data pipeline to ingest and process data from multiple sources (e.g., S3, Kinesis) to Redshift using Spark.

System Design/Architecturehard2.9 min readPremium
Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
EPAM
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
588 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Multi-source pipelines ingest from heterogeneous systems (S3, Kinesis, databases, APIs) into a unified store. The primary challenge is coordinating different latencies (batch vs. stream), schemas, and SLAs....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like EPAM. The answer also includes follow-up discussion points that interviewers commonly explore.

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