How Percentiles Work (and Why They're Better Than Averages)

In this tutorial, we will explore how to use TimescaleDB's percentile approximation hyperfunctions for time-series data analysis. We will cover the basics of percentiles and why they are useful, as well as the benefits of using percentile approximations over exact percentiles in PostgreSQL. We will also dive into the details of the underlying algorithms used by TimescaleDB's percentile approximation hyperfunctions and how to choose between them based on your specific use case. Finally, we will demonstrate how to use these hyperfunctions with real-world examples and discuss their potential applications in various industries. REFERENCES: 1. "TimescaleDB Documentation - Percentile Approximation Hyperfunctions" (https://docs.timescale.com/using-timescaledb/latest/how-to-guides/approximate-percentiles/) 2. "PostgreSQL Documentation - Aggregate Functions" (https://www.postgresql.org/docs/current/functions-aggregate.html) 3. "Two-Step Aggregation Design Patterns in PostgreSQL" (https://www.timescale.com/blog/two-step-aggregation-design-patterns-in-postgresql/) 4. "Introduction to Time Series Databases and TimescaleDB" (https://www.timescale.com/developers/book/introduction-to-time-series-databases-and-timescaledb/)

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