Design a Real-Time Analytics Pipeline for Mobile Events
This tests your grasp of low-latency streaming architectures. A good answer outlines ingestion (SDK to Kafka/Kinesis), real-time processing (Flink/Spark), and sinking to a fast OLAP database (Druid/ClickHouse). A red flag is proposing a batch-based ETL design.
This question tests your ability to design a low-latency streaming data system, balancing throughput, cost, and query performance for a "hot path" analytics use case. A strong answer outlines four stages: ingestion via SDK, buffering with a message queue like Kafka, real-time transformation with Flink, and sinking to a real-time OLAP database like Druid or ClickHouse. A common red flag is choosing a traditional data warehouse like Redshift, which often can't meet the sub-second query latency required for an interactive dashboard.
Read the original → dagster.io
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- #data engineering
- #streaming
- #analytics
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