Generate business analytics faster
Uncover unique business insights with historical backups
Optimize decision-making from accurate forecasts and trends
Deliver on AI/ML initiatives immediately
Tap into an alternative source of production data
Report on past data
Trend or forecast with on-demand historical data
Automatically populate AI/ML models with time-series data
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.
