Microsoft·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Microsoft system design round, focused entirely on a large-scale telemetry pipeline. The question had a lot of surface area and I'm not sure I covered it well enough given the time.

Questions Asked (1)

Design a distributed system that collects telemetry data from millions of devices, supporting both real-time streaming analytics and batch processing for offline aggregation. Walk through ingestion, storage, processing, and querying.

System DesignTechnical Trade-offsData Modeling

Author's notes

I spent too long on the ingestion layer and ran out of steam by the time we got to querying.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (data volume, latency, durability, query patterns) and then propose a high-level architecture that separates ingestion, storage, and processing layers. Walk through each component, emphasizing trade-offs and how the design meets both real-time and batch needs.

Pro tip: Show awareness of cost and operational complexity by discussing how to tier storage and use managed services where appropriate, and mention backpressure and exactly-once semantics as key challenges.

1. Clarify Requirements and Constraints

Ask about data volume (events per second), latency requirements for real-time analytics, retention policies, and query patterns. This ensures the design is tailored to actual needs.

2. Design Ingestion Layer

Propose a scalable ingestion pipeline using a distributed message queue (e.g., Kafka, Event Hubs) to handle millions of devices. Discuss protocols (MQTT, HTTP), device authentication, and data validation.

3. Design Storage Layer

Choose storage solutions for different needs: a time-series database or data lake for raw data, and a columnar store for analytics. Discuss partitioning, replication, and tiered storage for cost efficiency.

4. Design Processing Layer

Implement stream processing (e.g., Flink, Spark Streaming) for real-time analytics and batch processing (e.g., Spark, MapReduce) for offline aggregation. Highlight how to maintain consistency between the two paths.

5. Design Querying and Serving Layer

Provide APIs for querying real-time and historical data. Discuss indexing, caching, and query optimization. Mention how to handle aggregations and joins across streams and batches.

Key Points to Mention

Scalability and partitioning strategies for ingestion and storage
Trade-offs between latency, throughput, and cost
Exactly-once processing and idempotency in stream processing
Data modeling for time-series and analytical queries
Fault tolerance and backpressure handling
Use of managed cloud services (e.g., Azure Event Hubs, Azure Data Explorer) to reduce operational overhead

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.