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Explain how you would implement real-time analytics using a streaming platform like Kafka or Kinesis.

Spark/Big Datahard0.6 min readPremium
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
Adidas
Key Concepts Tested
optimizationpartitionsparksqlwindow
Expert AnswerPremium
114 wordsInterview-ready
**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....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Adidas. The answer also includes follow-up discussion points that interviewers commonly explore.

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