aggregatedByday

aggregatedByday is a data aggregation technique used to summarize time-stamped data at the day level. Each record with a timestamp is assigned to its calendar day, and one or more metrics are computed across all records within that day. The technique is widely used in time-series analytics and reporting to reduce granularity and reveal daily trends.

Implementation involves grouping by the date portion of the timestamp and computing statistics such as sum, count, mean, min, and max. In SQL this is typically done with functions that extract the date or truncate to the day, often with a specified time zone. In data processing libraries like Pandas or Spark, similar operations are performed by groupby with daily frequency or by resample('D').

Typically the result is a daily time series with the day as the index and the aggregated metrics as columns. Daily aggregates facilitate comparisons across days and enable dashboards and reports.

Considerations include time zone handling, as days are defined by a chosen zone; missing days may appear if the dataset has no observations for a day, in which case zero-filling or explicit gaps may be needed. DST shifts and leap days can also affect boundary definitions.

aggregatedByday is a common granularity in ETL pipelines and business intelligence work, and it often complements other aggregations such as aggregatedByhour or aggregatedByweek in a multi-granularity analytics framework.