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Interviewer: Let's start with a system design question. How would you design a real-time analytics pipeline for an e-commerce site that processes 10,000 events per second?
What technologies would you choose and why?
I would use Apache Kafka for ingestion since it handles high throughput well. Then, I'd use Apache Flink or Spark Streaming to process the events in real-time, windowing them by minute. Finally, I'd sink the aggregated data into a real-time OLAP database like ClickHouse or Apache Druid for sub-second query latency.
Feedback: Excellent choice of technologies. Kafka + Flink + Druid is a standard, robust stack for this exact use case.
Follow-up: What happens if one of your Flink task managers crashes? How do you ensure exactly-once processing?
Our AI agents are trained on real interview data from Amazon, Google, Databricks, and 94+ more companies. They don't just chat - they interview, evaluate, and teach like senior engineers.
FAANG Bar Raiser
Conducts realistic mock interviews using questions from real FAANG interviews. Evaluates your answers, asks follow-ups, and delivers a hire/no-hire verdict.
βHow would you handle data skew in a Spark job processing 500M rows?β
Good understanding of salting technique. Mention broadcast joins for small tables.
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βPaste your answer to any interview questionβ
Overall: 8/10 - Hire. Add trade-off discussion for a 10.
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Breaks down complex data engineering concepts into simple, interview-ready explanations with analogies, code snippets, and follow-up questions.
βExplain the medallion architecture in a data lakehouseβ
Bronze (raw) β Silver (cleaned) β Gold (business-ready). Think: factory assembly line.
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