**Impact**: Structured Streaming cannot recover. Query fails or restarts from scratch. Risk of duplicate processing (reprocess from earliest) or data loss (if starting fresh with no offset). **Why Checkpoint Matters**: Stores processed offsets and state. Exactly-once semantics...
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.
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.
Impact: Structured Streaming cannot recover. Query fails or restarts from scratch. Risk of duplicate processing (reprocess from earliest) or data loss (if starting fresh with no offset).
Why Checkpoint Matters: Stores processed offsets and state. Exactly-once semantics depend on it.
Recovery: For Kafka—reset offsets or start from earliest/latest. Reprocess may cause duplicates; idempotent sink required.
Prevention: Checkpoint in durable storage (S3, DBFS). Document recovery. Never delete manually. Backup for critical streams.
Cost Implications: Lost checkpoint = reprocess = double cost for that period. Durability is cheap insurance.
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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.