Specrova
    Case Study
    Nov 24, 202510 min read

    How We Built a Real-Time Analytics Pipeline in 3 Weeks

    A case study on building a streaming data pipeline using Kafka and ClickHouse for a Series A analytics startup.

    SC

    Sarah Chen

    Specrova Team

    How We Built a Real-Time Analytics Pipeline in 3 Weeks

    A Series A analytics startup needed to process 10M events per day in real-time. Here's how we built their pipeline in just 3 weeks.

    The Requirements

    Sub-second query latency on aggregations across billions of rows, with real-time ingestion from multiple sources.

    The Architecture

    We used Kafka for ingestion, ClickHouse for storage and querying, and a thin Node.js API layer for the frontend.

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    Results

    • 10M+ events/day processed in real-time
    • P95 query latency under 200ms
    • Deployed to production in 3 weeks
    • Cost: $800/month for the entire pipeline

    Conclusion

    Real-time analytics doesn't have to be expensive or complex. The right architecture choices can give startup-scale results at startup-scale costs.

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    Written by Sarah Chen

    Co-founder & CTO at Specrova. Previously Senior Engineer at Stripe. Passionate about scalable architectures and helping founders make smart technical decisions.

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