Modern systems generate large volumes of time series data due to advancements in hardware and edge instrumentation, allowing sensors and applications to capture data frequently. While this influx of data can reveal early warning signs and enhance real-time system understanding, it also strains dashboards, queries, and storage, leading to increased operational costs and reduced visibility. To manage this, downsampling is used to reduce data volume by summarizing older data into aggregates over defined intervals, maintaining recent data at full resolution for short-term analysis while older data becomes more manageable. This method provides a sustainable way to perform long-term time series analysis, preserving trends and optimizing storage and compute resources. InfluxDB 3 offers a downsampling plugin that automates this process, allowing teams to configure rules for summarizing data, thus improving the efficiency of queries and maintaining a clear historical record without manual intervention. This approach helps organizations manage data growth effectively, ensuring systems remain responsive and insights are easily accessible as environments scale.