Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why Incremental**: Full table scans on 100M+ row RDBMS tables cause locks, replication lag, and hour-long loads. Incremental captures only delta—minutes instead of hours. **Modes**: (1) **append**—check column (e.g., id) for new rows; append-only tables. (2)...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (sql) will help you answer variations of this question confidently.
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. The expert answer includes a code example that demonstrates the implementation pattern.
Why Incremental: Full table scans on 100M+ row RDBMS tables cause locks, replication lag, and hour-long loads. Incremental captures only delta—minutes instead of hours.
Modes: (1) append—check column (e.g., id) for new rows; append-only tables. (2) lastmodified—timestamp column; captures updates and inserts; use --merge-key to upsert.
Architectural Logic: Store --last-value in a control table or file; next run reads it. Without idempotent storage, reruns duplicate data.
Scalability Trade-offs: --split-by must be indexed; unindexed column causes full scan per mapper. --num-mappers > source connection limit causes connection exhaustion.
Cost Implications: Reduces source DB load 80–95% for large tables; enables hourly vs. daily sync. Trade-off: complexity of merge logic for lastmodified.
sqoop import --connect jdbc:mysql://host/db --table orders \
--incremental lastmodified --check-column last_updated \
--last-value '2024-01-01' --merge-key id --target-dir /data/orders
Pro-Move: 'We persist last-value in Delta; job reads it, runs Sqoop, then updates.' Red Flag: lastmodified without --merge-key—creates duplicate rows for updates.
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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.