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Can you explain the concept of incremental loading in Sqoop and how to use it for job processing?

Spark/Big Dataeasy0.5 min read

**Why incremental loading matters**: Full dumps of large tables waste bandwidth and time; incremental = only new/changed rows. **Sqoop incremental**: `--incremental append` for insert-only tables (e.g., logs); `--incremental lastmodified` for tables with update column. Use...

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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
Infosys

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Infosys. 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
91 words

Why incremental loading matters: Full dumps of large tables waste bandwidth and time; incremental = only new/changed rows. Sqoop incremental: --incremental append for insert-only tables (e.g., logs); --incremental lastmodified for tables with update column. Use --check-column and --last-value; store last value in metastore or file for next run. Scalability trade-offs: Append = simple; lastmodified requires consistent timezone and indexed check column. Cost implications: Incremental = 10–100x less data transferred; critical for daily sync of 100GB+ tables. Best practice: Index check column; use lastmodified for CDC; schedule incremental jobs; handle timezone consistently.

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