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Home/Questions/Spark/Big Data/Compare HDFS and cloud-based storage systems in terms of scalability and performance.

Compare HDFS and cloud-based storage systems in terms of scalability and performance.

Spark/Big Dataeasy0.5 min readPremium

**Why comparison matters**: Choice affects cost model, elasticity, and ops. **HDFS**: Scale by adding nodes; fixed cluster size; data locality for compute. Throughput limited by cluster; replication = 3x storage. **Cloud (S3, GCS)**: Effectively unlimited scale; pay-per-use; no...

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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
Swiggy

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Swiggy. While less common, it tests deeper understanding that distinguishes strong candidates.

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
97 words

Why comparison matters: Choice affects cost model, elasticity, and ops. HDFS: Scale by adding nodes; fixed cluster size; data locality for compute. Throughput limited by cluster; replication = 3x storage. Cloud (S3, GCS): Effectively unlimited scale; pay-per-use; no cluster to manage. High throughput with proper concurrency; no locality (network fetch). Scalability trade-offs: HDFS = predictable perf with locality; cloud = elastic but network latency. Cost implications: HDFS = CapEx + ops; cloud = OpEx, egress cost. For new pipelines, cloud preferred; HDFS when on-prem or latency-sensitive. Best practice: Use cloud storage; optimize for request patterns (prefix, multipart).

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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