Your analytics team is integrating web-server logs coming from data centers in New York, Chicago, and Los Angeles. Each record currently stores its event time as the string "2025-11-02 01:30:15" with no zone designator. During the November daylight-saving switch-back, that local clock time occurs twice, so the parsed datetimes cannot be ordered uniquely and a join with European event data fails. Before any joins or aggregations, which action is the most appropriate first step to eliminate the ambiguity and create a single, comparable timeline for the entire data set?
Keep the original local timestamp strings and add a Boolean column indicating whether each record was captured during daylight-saving time.
Extract only the date portion ("YYYY-MM-DD") and aggregate logs at a daily granularity, discarding the time-of-day detail.
Remove all records whose local time falls inside the daylight-saving transition window and later re-create them with linear interpolation.
Parse the string in the server's local zone and convert the resulting datetime to UTC, storing it in ISO 8601 format with a trailing "Z".
Parsing the local strings in their original time zones and immediately converting every value to Coordinated Universal Time (UTC) removes the duplicate "fall-back" hour. ISO 8601's trailing "Z" (or an explicit offset) records the relationship to UTC, so each moment is represented exactly once and can be compared or joined across regions. Dropping records discards data, adding a daylight-saving flag still leaves two identical local timestamps, and stripping the time of day prevents any sub-daily analysis; none of those approaches solves the fundamental ambiguity.
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What does UTC stand for, and why is it important in data analysis?
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How does the daylight-saving 'fall-back' hour create ambiguity in timestamps?