Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**File Discovery**: Directory listing on S3/ADLS has latency and cost (LIST requests). Use **cloud file notifications** (S3 Events, EventGrid) when available—faster and cheaper at scale. **Schema Inference**: Inferring schema from files adds overhead. Provide explicit schema...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates.
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.
File Discovery: Directory listing on S3/ADLS has latency and cost (LIST requests). Use cloud file notifications (S3 Events, EventGrid) when available—faster and cheaper at scale.
Schema Inference: Inferring schema from files adds overhead. Provide explicit schema for static schemas.
Checkpoint: Durable storage (S3, DBFS); avoid small-object problem. Checkpoint growth with many files.
Backlog: Initial load of millions of files—use trigger(availableNow) in batches; avoid single run.
Parallelism: Limited by new file arrival; more files = more parallelism.
Scalability Trade-offs: Notification-based scales to millions of files; listing scales poorly.
Cost Implications: S3 LIST costs at scale; notifications are cheaper. Checkpoint in same region as source.
Pro-Move: 'S3 events + Auto Loader; we ingest 1M files/day with sub-min latency.' Red Flag: Schema inference on every batch—provide schema, 3x faster.
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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.