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Home/Questions/Spark/Big Data/Explain how you would use Kafka Connect to ingest data from a relational database into Kafka while ensuring minimal latency and exactly-once semantics.

Explain how you would use Kafka Connect to ingest data from a relational database into Kafka while ensuring minimal latency and exactly-once semantics.

Spark/Big Datahard0.5 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Kafka Connect for DB ingestion: Use Debezium or JDBC Source connector. For minimal latency: set batch.size small, poll frequently; use...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Dunnhumby
Key Concepts Tested
optimizationpartitionsql

Why This Question Matters

This hard-level Spark/Big Data 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 (optimization, partition, sql) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
108 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Kafka Connect for DB ingestion: Use Debezium or JDBC Source connector. For minimal latency: set batch.size small, poll frequently; use multiple tasks for parallelism. Exactly-once: enable idempotence; use transactional producer; store connector offsets in Kafka (Kafka Connect manages this); ensure idempotent writes to sink. Debezium provides CDC with exactly-once via Kafka transactions. Config: connector.class=io.debezium.connector.postgresql.PostgresConnector; plugin.name=pgoutput. Best practices: use single-partition tables or careful partitioning; monitor lag; validate offset handling.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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