Time Bucketing
Time bucketing is the practice of grouping time-series data into fixed time intervals, such as per minute, per hour, or per day, and aggregating within each interval. It is how you turn a flood of raw timestamped points into a readable trend.
Why time bucketing is everywhere in analytics
Raw time-series data is often too granular to look at directly. Millions of per-second readings are noise to the human eye. Time bucketing rolls them up into intervals: average temperature per hour, request count per minute, total volume per day. The result is something you can chart and reason about.
Almost every time-series dashboard and report relies on it. It is the operation behind "show me hourly averages for the last week" or "daily totals this month". Good time-series databases provide dedicated functions to make it fast and simple.
It is closely related to downsampling, but it happens at query time to shape results, rather than permanently discarding detail.
How Arc handles Time Bucketing
Arc supports time bucketing directly in SQL through functions like time_bucket and date_trunc, so you can roll raw time-series data into clean per-interval summaries at query time, while keeping the full-resolution data underneath.
Arc is a high-performance columnar database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.