**Auto Loader**: Incremental file ingestion from cloud storage. Detects new files; processes only new; checkpoint for exactly-once. **How**: (1) Directory listing or cloud notifications (S3 Events, etc.) to detect new files. (2) Checkpoint tracks processed files. (3) 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.
Auto Loader: Incremental file ingestion from cloud storage. Detects new files; processes only new; checkpoint for exactly-once.
How: (1) Directory listing or cloud notifications (S3 Events, etc.) to detect new files. (2) Checkpoint tracks processed files. (3) Schema evolution optional. (4) Trigger: once, availableNow, or continuous.
Why vs. Batch Read: Batch rereads all; Auto Loader incremental. At 1M files/day, batch is infeasible.
Scalability Trade-offs: Notifications scale; listing doesn't. Provide schema when static.
Cost Implications: Notifications cheaper than LIST at scale. Checkpoint in durable storage.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.