Design a Near Real-Time Analytics Pipeline
Tests your ability to design a low-latency data system and articulate trade-offs. A good answer covers ingestion (Kafka), processing (Flink), storage (Druid), and visualization (Grafana), contrasting the architecture's low latency with a batch setup.
This tests your ability to design an end-to-end, low-latency data system and articulate trade-offs against batch processing. A strong answer outlines a four-stage pipeline: ingestion with a durable queue (Kafka), stream processing for stateful aggregation (Flink), storage in a specialized OLAP database (Druid/ClickHouse) for fast queries, and visualization (Grafana). The key is to justify each choice based on latency and throughput needs. A red flag is naming tools without explaining *why* they fit the real-time requirements.
Read the original → evermethod.com
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- #system design
- #analytics
- #streaming
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