How to proactively manage long-term data storage with downsampling

TimescaleDB allows users to reduce storage costs by downsampling their time-series data without sacrificing long-term analytical value. Downsampling involves applying mathematical aggregation functions to roll up granular data sets into coarser periods, reducing storage needs while maintaining data accuracy. By combining downsampling with continuous aggregates and data retention policies, users can save money on storage while preserving the ability to perform analytical queries over longer time horizons. The process of downsampling involves creating a continuous aggregate view that produces summaries at desired intervals, followed by adding a data retention policy to remove finer-grained data after a specified period. This approach enables users to extract long-term analytical value from their monitoring data without compromising performance or storage costs.

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