Initially, we will aggregate the data. If we look at the data, we see that four requests were sent on January 4, 2020. How would you aggregate this data to analyze request patterns?

When aggregating request data, the goal is to transform raw logs into meaningful metrics. First, identify the key dimensions: date, hour, request type, or user ID. In SQL, you can group by the date part of the timestamp and count the number of requests: SELECT DATE(request_time) AS request_date, COUNT(*) AS request_count FROM requests WHERE request_time >= '2020-01-04' AND request_time < '2020-01-05' GROUP BY DATE(request_time); This will give you the total number of requests on January 4, 2020. If you need finer granularity, group by hour: GROUP BY DATE(request_time), HOUR(request_time). In Spark, you can use df.groupBy(F.to_date('request_time')).count() or df.groupBy(F.window('request_time', '1 hour')).count() for time‑windowed aggregates. Once you have the counts, you can compute additional metrics such as average requests per hour, peak load times, or compare against other days. Visualizing the aggregated data in a dashboard (e.g., Grafana, Tableau) helps spot trends. This approach scales to large datasets because both SQL and Spark use distributed execution and can handle millions of rows efficiently.