Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Hadoop's architecture centers on HDFS (storage) + YARN (resource scheduling) + MapReduce (compute model). Components: Namenode (single point for metadata—scalability ceiling for namespace); Datanodes (block storage, 128MB default blocks, 3x replication);...
Red Flag: Proposing Hadoop for a new project without articulating why it beats S3+EMR—often indicates outdated playbook. Pro-Move: Quantify TCO (hardware + ops + eng time) for 3yr; show when cloud-native pays off.
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik, Infosys. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) 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.
Hadoop's architecture centers on HDFS (storage) + YARN (resource scheduling) + MapReduce (compute model). Components: Namenode (single point for metadata—scalability ceiling for namespace); Datanodes (block storage, 128MB default blocks, 3x replication); ResourceManager/NodeManagers for CPU/memory allocation. Why it matters architecturally: HDFS scales linearly with nodes but metadata scale is bounded by Namenode heap; YARN allows multi-tenant workloads but adds operational overhead. MapReduce is batch-only—no interactive or streaming without additional systems. Cost implications: CAPEX-heavy for on-prem hardware; ops cost for HA (active/standby Namenode, fencing); storage inefficiency (3x replication vs. erasure coding). Scalability trade-offs: Hadoop suits on-prem, cost-sensitive, regulated environments where data locality and predictable ops are valued; cloud-native (S3+EMR, Databricks) offers elastic scaling, managed ops, and pay-per-use. Best practice: use Hive/Spark over raw MapReduce; consider cloud alternatives unless regulatory or cost constraints mandate on-prem.
Red Flag: Proposing Hadoop for a new project without articulating why it beats S3+EMR—often indicates outdated playbook. Pro-Move: Quantify TCO (hardware + ops + eng time) for 3yr; show when cloud-native pays off.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.