Data Modeling Guide for Real-Time Analytics with ClickHouse
5 days ago
- #data-modeling
- #real-time-analytics
- #ClickHouse
- ClickHouse provides real-time analytics with sub-second query responses on billions of records.
- Key features include column-oriented storage, advanced compression, and vectorized query execution for performance.
- Data modeling in ClickHouse focuses on minimizing query-time complexity through denormalization and materialized views.
- ClickHouse supports real-time aggregation strategies like incremental materialized views and refreshable materialized views.
- Optimization techniques include partitioning, predicate pushdown, and pre-aggregation with AggregatingMergeTree.
- Storage efficiency is achieved through data sketches, rollup strategies, and statistical sampling.
- Schema management includes table projections and best practices for schema evolution.
- Time series optimization emphasizes UTC storage and efficient querying.
- ClickHouse integrates well with tools like Rill for visualization and metrics management.
- Choosing the right modeling strategy depends on data volume, latency needs, and team capabilities.