As a creator of a database with unique time-series functionality, we explored the aspects of time-series data, including forecasting trends and performing meaningful analysis. To analyze our fictional web application's user login patterns, we updated the "users" table to log timestamps for every login, not just the latest one. This change allowed us to answer questions like what kind of devices are most frequently used by individual users and across all users, and what time of day are users the most active. With these new details logged, we can start querying the data for insights using time-series databases like Timescale. These databases help with time-series workloads in two crucial ways: handling large volumes of data and providing specialized functions to query aspects of data where time is a primary component. We demonstrated three example queries that utilize these functions, such as time_bucket() and last(), to analyze our users' usage behavioral patterns. By using these tools, we can quickly transform our ability to understand how our web application is used and make decisions faster.