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Time Series Data: Storage, Querying, and Anomaly Detection

Metrics, IoT sensor readings, and financial tick data are all time series. Learn how to store, compress, and query time-ordered data at massive scale.

What Makes Time Series Special

Time series data has unique characteristics: it is append-only, arrives in chronological order, and queries almost always involve time-range filters. Relational databases handle these patterns poorly — specialised time series databases excel.

Time Series Database Options

  • InfluxDB: Purpose-built TSDB with a flux query language. Excellent for metrics and monitoring.
  • TimescaleDB: PostgreSQL extension for time series. Benefit from SQL familiarity with TSDB performance.
  • Apache Druid: Optimised for sub-second OLAP queries on high-ingestion event streams.

Anomaly Detection

Use Z-score, IQR, or Prophet (Facebook’s forecasting library) to detect anomalies in time series data automatically, alerting on unexpected deviations from seasonal baselines.

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