Reviewed by Aditya Kumar · Last reviewed 2026-03-24
S3: Object storage—durable, highly available, decoupled from compute. Pay-per-use, virtually unlimited scale. Eventual consistency (now strong for new overwrites). No data locality. HDFS: Distributed file system—block-based, colocated with compute nodes. Data locality reduces...
Red Flag: Saying 'HDFS is better' or 'S3 is better' without context. Pro-Move: Discussing migration trade-offs and when each is appropriate—shows architectural judgment.
This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like EY, Incedo, Tech Mahindra. While less common, it tests deeper understanding that distinguishes strong candidates.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
S3: Object storage—durable, highly available, decoupled from compute. Pay-per-use, virtually unlimited scale. Eventual consistency (now strong for new overwrites). No data locality. HDFS: Distributed file system—block-based, colocated with compute nodes. Data locality reduces network I/O; strong consistency. Requires cluster management. Why it matters: S3 enables cloud-native, serverless patterns (Lambda, Glue, Athena); HDFS optimizes for batch processing where locality matters. Scalability: S3 scales transparently; HDFS requires adding nodes. Cost: S3 has no compute cost when idle; HDFS clusters run 24/7. Trade-off: Migrating from HDFS to S3 reduces ops but may require query and tool changes (e.g., Hive to Athena). For new workloads, S3 is the default; HDFS remains for on-prem Hadoop or legacy systems requiring locality.
Red Flag: Saying 'HDFS is better' or 'S3 is better' without context. Pro-Move: Discussing migration trade-offs and when each is appropriate—shows architectural judgment.
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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 3 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.