**Strategies**: (1) Scale consumers—add instances up to partition count. (2) Increase parallelism—more threads per consumer. (3) Batch processing—increase fetch size; process in batches. (4) Optimize processing—async I/O; avoid blocking calls. (5) Add partitions—if...
This medium-level SQL question appears frequently in data engineering interviews at companies like Dunnhumby. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Strategies: (1) Scale consumers—add instances up to partition count. (2) Increase parallelism—more threads per consumer. (3) Batch processing—increase fetch size; process in batches. (4) Optimize processing—async I/O; avoid blocking calls. (5) Add partitions—if under-partitioned. (6) Rebalancing—ensure even partition assignment. (7) Kafka Streams/Flink for stateful processing. (8) Separate slow and fast topics. Why: Lag = throughput < production rate. Scalability: Each partition = max one consumer per group. Cost: More consumers = more compute; tune before scaling.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL 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.