**Purpose**: Checkpoint stores (1) processed offsets (Kafka, files) and (2) state (aggregations, joins) for Structured Streaming. Enables exactly-once and failure recovery. **Why Critical**: No checkpoint = no recovery. Restart = reprocess from start or lose state. Exactly-once...
This hard-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. Mastering the underlying concepts (join) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Purpose: Checkpoint stores (1) processed offsets (Kafka, files) and (2) state (aggregations, joins) for Structured Streaming. Enables exactly-once and failure recovery.
Why Critical: No checkpoint = no recovery. Restart = reprocess from start or lose state. Exactly-once impossible.
Requirements: Durable storage (DBFS, S3). Unique per query. Never delete.
Scalability Trade-offs: Checkpoint can grow; monitor. Same path = same query identity.
Cost Implications: Checkpoint storage cheap. Lost checkpoint = reprocess = 2x cost. Backup for critical streams.
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Analyze My Answer β 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.