Spent the first few minutes just trying to scope it out because 'reporting dashboard' could mean a dozen different things.
Start by clarifying the requirements and scope of the malware scanner reporting dashboard, focusing on the key users (security engineers, incident responders) and their needs. Then outline a high-level architecture covering data ingestion, processing, storage, and visualization, while discussing trade-offs and scalability. Finally, dive into specific components like real-time alerting, data retention, and query performance, ensuring alignment with Uber's scale and security constraints.
Pro tip: Emphasize the importance of designing for both real-time and historical analysis, and discuss how you would handle data volume and velocity given Uber's massive scale. Also, highlight security and privacy considerations, such as access control and data anonymization, to demonstrate a mature understanding of the domain.
Ask questions to understand the dashboard's purpose, target users, key metrics, data sources, and non-functional requirements like latency, throughput, and retention.
Sketch the end-to-end system: data collection from scanners, ingestion pipeline, processing (e.g., aggregation, enrichment), storage (e.g., time-series DB, data lake), and visualization layer.
Discuss critical components in detail: real-time alerting, query engine, data partitioning, and how to ensure scalability and fault tolerance.
Analyze trade-offs between consistency and availability, batch vs. stream processing, and cost vs. performance. Explain how the design scales with Uber's data volume.
Address security (access control, encryption) and monitoring of the dashboard itself (e.g., health checks, alerting on failures).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.