Start by clarifying requirements: scale (number of meetings per day, concurrent participants), data types (audio, video, transcripts), and analysis goals (sentiment, topic extraction, compliance). Then propose a high-level architecture with ingestion, storage, processing, and analysis layers, and dive into trade-offs for key components like real-time vs batch processing and data modeling for customer insights.
Pro tip: Emphasize data privacy and compliance (e.g., GDPR, CCPA) early, especially for customer data, and suggest anonymization or encryption to build trust. Also, mention cost optimization by using tiered storage and spot instances for batch processing.
Ask questions to understand scale, latency needs, data retention, and analysis types. This ensures the design meets actual business needs and avoids over-engineering.
Outline components: ingestion (WebRTC, SIP), storage (object store for recordings, database for metadata), processing (transcription, NLP), and analysis (dashboards, APIs). Draw a simple diagram.
Design schemas for meetings, participants, transcripts, and customer interactions. Choose appropriate databases (e.g., relational for metadata, NoSQL for transcripts, data lake for raw recordings).
Describe batch vs stream processing for transcription and analysis. Discuss tools like Kafka, Spark, or serverless functions, and how to handle failures and retries.
Discuss trade-offs: cost vs performance, consistency vs availability, and how to scale each component. Mention monitoring, security, and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.