Choose a project where you owned the streaming pipeline end-to-end, and structure your answer around the problem, architecture, design rules, and outcomes. Focus on the 'why' behind your design decisions and how they addressed scalability, reliability, and data consistency.
Pro tip: Uber values real-time data and reliability; emphasize how your design rules prevented data loss, ensured exactly-once processing, and enabled seamless scaling. Quantify the impact with metrics like throughput, latency, and cost savings.
Briefly describe the project's goal, the data volume, and the business need for streaming. Highlight why batch processing was insufficient.
Explain the high-level architecture: data sources, ingestion, processing, and sinks. Mention technologies used (e.g., Kafka, Flink, Spark Streaming) and why they were chosen.
Detail the key design rules you enforced, such as partitioning strategy, backpressure handling, exactly-once semantics, and schema evolution. Discuss trade-offs made (e.g., latency vs. throughput).
Describe a significant challenge (e.g., data skew, out-of-order events) and how your design rules helped overcome it. Explain any iterations or improvements.
Summarize the results: improved latency, reduced cost, increased reliability. Share what you learned and how it influenced future projects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.