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Data modeling guide for real-time analytics with ClickHouse
Learn how to build sub-second real-time analytics with ClickHouse. Complete guide covering data modeling strategies, optimization techniques, and practical S3-to-dashboard examples.
Now that we understand the data flow requirements for real-time analytics such as fast ingestion, efficient transformation, and sub-second query responses, let’s explore how ClickHouse specifically addresses these challenges through its modeling approaches. Ultimately, the choice between native ClickHouse modeling, external ETL pipelines, or BI tool integration comes down to balancing three key factors: data freshness requirements, transformation complexity, and team capabilities. ClickHouse’s native approach eliminates traditional ETL overhead for most real-time use cases, but the flexibility to layer additional tools when needed ensures your analytics architecture can evolve with your business requirements.
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