Why Time-Series Data Needs Downsampling Not Raw Storage

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Why Time-Series Data Needs Downsampling Not Raw Storage

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Episode 132 of Database Tech with Fexingo dives into a common but costly mistake: storing every raw time-series data point forever. Lucas and Luna examine a smart-meter company collecting one-minute readings from 10,000 meters, producing 5.2 billion data points per year. They break down the storage costs, query slowdowns, and why downsampling to hourly aggregates preserves insight while slashing infrastructure bills. The conversation covers retention policies, downsampling strategies (average, min, max, count), and real-world implementations using InfluxDB and TimescaleDB. A must-listen for engineers managing IoT, monitoring, or any time-series pipeline. #TimeSeries #Downsampling #DataStorage #Database #InfluxDB #TimescaleDB #IoT #DataRetention #Aggregation #Performance #CostOptimization #SmartMeter #DataEngineering #Technology #FexingoBusiness #BusinessPodcast #DatabaseTech #SQLNoSQL Keep every episode free: buymeacoffee.com/fexingo

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