Sub-Second Queries on Billions of Rows: Real-Time Analytics with ClickHouse

This guide demonstrates building real-time analytics applications with ClickHouse, achieving sub-200-millisecond query responses on billions of weather records. It covers data ingestion, advanced techniques like statistical sampling and pre-aggregation, and showcases a complete workflow using Rill, ingesting NOAA weather data from S3 and visualizing it. ClickHouse's columnar storage, advanced compression, and vectorized query execution deliver blazing-fast performance, making it ideal for real-time analytics. The article explores the trade-off between data freshness and accuracy, detailing ClickHouse modeling strategies (denormalization, dictionaries, incremental materialized views). A practical example using ClickHouse, S3, and Rill for real-time weather data analysis is presented.
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