**Approach**: Time-based join—for each ticket_date, get max rate_timestamp <= ticket_date. CROSS APPLY / LATERAL with ORDER BY rate_timestamp DESC LIMIT 1. Spark: asof join (merge_asof in Pandas); or join with condition + window for latest....
This medium-level General/Other question appears frequently in data engineering interviews at companies like Expedia. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, spark, window) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Approach: Time-based join—for each ticket_date, get max rate_timestamp <= ticket_date. CROSS APPLY / LATERAL with ORDER BY rate_timestamp DESC LIMIT 1. Spark: asof join (merge_asof in Pandas); or join with condition + window for latest. Key: 'latest' = max timestamp <= ticket date.
This answer is partially locked
Unlock the full expert answer with code examples and trade-offs
Practice real interviews with AI feedback, track progress, and get interview-ready faster.
Pro starts at $24/mo - cancel anytime
Get the most asked SQL questions with expert answers. Instant download.
No spam. Unsubscribe anytime.
Paste your answer and get instant AI feedback with a FAANG-level improved version.
Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked General/Other interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.