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Can Presto work with Near Real-Time Data (Streaming Data Source)?

Spark/Big Datahard0.5 min readPremium

**Why this matters**: Presto/Trino is an MPP query engine—designed for ad-hoc queries over *stored* data. **Native streaming**: No—Presto does not consume Kafka/Kinesis directly. **Near real-time options**: (1) Query frequently updated tables (e.g., Delta with 1-min...

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This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Walmart. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (lakehouse, spark) will help you answer variations of this question confidently.

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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

101 words

Why this matters: Presto/Trino is an MPP query engine—designed for ad-hoc queries over stored data. Native streaming: No—Presto does not consume Kafka/Kinesis directly. Near real-time options: (1) Query frequently updated tables (e.g., Delta with 1-min micro-batches); (2) Kafka connector for bounded reads ( snapshot queries); (3) Materialized views refreshed on schedule. Latency = refresh interval + query time. Scalability trade-offs: Streaming = Kafka Streams, Flink, Spark Structured Streaming. Presto for batch/interactive over lake/warehouse. Cost implications: Running Presto over streaming tables = repeated full scans; incremental tables reduce cost. Best practice: Use Presto for lakehouse querying; separate streaming stack for sub-minute latency.

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