I started with the data ingestion side, talking through polling vs webhooks and how often the USDA dataset actually updates (not that often, so a scheduled batch pull made more sense than anything real-time).
Start by clarifying requirements: what nutrition data is needed, how fresh it must be, and what scale Google's nutrition product operates at. Then design a pipeline that ingests USDA data, transforms it into a canonical schema, stores it for low-latency serving, and exposes it via an API that the product can consume. Emphasize reliability, data quality, and scalability.
Pro tip: Discuss how you would handle data updates and versioning from the USDA source, and how to reconcile conflicting nutritional values across different USDA datasets. Showing awareness of data consistency and provenance will impress interviewers.
Ask about the scope: which USDA datasets (e.g., FoodData Central), required data fields, update frequency, expected query volume, and latency requirements. Confirm whether real-time or batch processing is needed.
Propose a pipeline to fetch USDA data via their API or bulk downloads, handle rate limits, and schedule periodic updates. Consider using a message queue or batch processing framework like Apache Beam for scalability.
Define a canonical nutrition schema that maps USDA data to Google's product needs. Include normalization, unit conversion, and data cleaning. Discuss how to handle missing or inconsistent values.
Choose a storage solution (e.g., Bigtable, Spanner, or a cache like Memcached) for low-latency reads. Design an API layer that serves nutrition data with high availability and low latency, possibly using a CDN for global distribution.
Outline monitoring for data freshness, API latency, and error rates. Plan for A/B testing and feedback loops to improve data quality and coverage over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.