Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Real-time analytics with Kafka/Kinesis: (1) Ingest—Kafka Connect or Kinesis Agent for source ingestion; (2) Process—Spark Streaming,...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Adidas. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, spark) 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.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Real-time analytics with Kafka/Kinesis: (1) Ingest—Kafka Connect or Kinesis Agent for source ingestion; (2) Process—Spark Streaming, Flink, or ksqlDB for stream processing; (3) Store—write to Delta Lake, ClickHouse, or OLAP; (4) Serve—dashboards (Grafana), APIs. Architecture: Kafka topics to Consumer (Spark Streaming micro-batches or Flink) to aggregations to sink. Example: structured streaming with Kafka source, windowed aggregations, Delta sink. Best practices: size partitions for parallelism; use exactly-once where needed; design for backpressure; monitor consumer lag.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Real-time without exactly-once. Pro-Move: 'Checkpoint; idempotent sink; monitor lag.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.