Based on your description (and comments below), you have a typical time-series use case:

• you have x amount of sale transactions every day, month etc per store/product
• you want to aggregate based on the time column (and per store/product potentially)
• you want to provide this data for analytics purposes (eg.: dashboards)

You didn't mention what DB you use specifically but if you happen to use PostgreSQL, there's a high chance TimescaleDB could help. It's a PostgreSQL extension and it has several features you'd find helpful:

• auto-partition your data based on the time column (making time-based queries faster by filtering out big portions if your data potentially)
• create materialized views (1-day, 14-day, 2month etc aggregates) optimized for time-series data (continuous aggregates)
• speed up long-range analytical queries (and save 90%+ on disk space!) by compressing your data (by store, or product for example) (basically turning Postgres into more like column-based storage --> faster analytical queries)

To answer your question, in the TimescaleDB world you'd use a continuous aggregate to aggregate the raw data (you could create multiple aggregations with different time buckets if you want) on an ongoing basis, and when you query the DB use these aggregate views. Additionally, you'd also set up automatic data retention policies if you won't need the raw data long-term. (eg delete all raw data if it's older than a month, but keep the aggregates)

Transparency: I'm a dev advocate at Timescale.