I knew the broad strokes but fumbled the specifics a bit.
Acknowledge that PySpark is generally preferred for its speed, ease of use, and rich API, but identify specific scenarios where Hadoop MapReduce still has advantages. Focus on trade-offs like batch processing of massive datasets, existing Hadoop investments, and operational simplicity in certain contexts.
Pro tip: Emphasize that the choice often depends on the existing ecosystem and team expertise, not just technical merits. Showing awareness of total cost of ownership and migration efforts demonstrates maturity.
Start by stating that the decision depends on factors like data size, latency requirements, existing infrastructure, and team skills. This shows you consider the broader picture.
Discuss scenarios where Hadoop MapReduce excels: extremely large-scale batch processing, environments with existing Hadoop clusters, and cases where disk-based processing is acceptable.
Contrast with PySpark's advantages: in-memory computation, interactive analysis, and support for iterative algorithms. Explain why PySpark is usually preferred for these.
Mention non-technical aspects like cost, maintenance, and team familiarity. For example, if a company already has a Hadoop ecosystem, using MapReduce might be simpler.
Summarize that while PySpark is often the default, Hadoop remains relevant in specific legacy or large-scale batch scenarios. Avoid absolute statements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.