Time-series data refers to a type of data that is collected in a time-series format, or data taken at regular intervals. These regular timestamps simplify reporting at a point-in-time, across a range of time, or to analyze patterns for trends or predictions. An example of time series data is the price of Apple stock each day over the last six months.

Time-series data allows for the creation of comprehensive reports that include past changes and historical trends. By quickly generating time-specific records and data sets, you can gain a holistic view of your data over time. This capability enables efficient trend analysis, forecasting, and decision-making using standard query and reporting tools without the need for additional data preparation or storage solution

Data warehouses like Snowflake may not have snapshots of the data at all and could be “current view” only. If the datawarehouse is being used for snapshots, it’s unlikely that it will will have all fields, for all objects for all snapshots (only certain objects and fields are likely to be mapped) which would have been based on the past business needs.