Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. HDFS stores data by splitting files into blocks (default 128MB), replicating blocks (default 3x) across DataNodes. NameNode tracks block...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
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.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
HDFS stores data by splitting files into blocks (default 128MB), replicating blocks (default 3x) across DataNodes. NameNode tracks block locations and metadata. Client writes to first replica; that node pipelines to others. Reads are from nearest replica. Rack awareness: spread replicas across racks for fault tolerance. Example: 1GB file yields 8 blocks, 24 total replicas. Best practices: use block size 128–256MB for large files; ensure replication factor 3 in production; monitor NameNode memory; use HDFS Federation for very large clusters; consider Erasure Coding for cold storage to save space.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Block size too small. Pro-Move: '128–256MB blocks; 3x replication; erasure coding cold.'
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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.