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Home/Questions/Cloud/Tools/Describe how Adidas could use S3 and Athena to analyze clickstream data.

Describe how Adidas could use S3 and Athena to analyze clickstream data.

Cloud/Toolshard0.4 min readPremium

Architecture: Ingestion via API Gateway + Lambda or Kinesis → S3 landing zone (JSON/Parquet) partitioned dt=YYYY-MM-DD. Glue Crawlers or manual schema → Athena tables. Query: Funnels, sessions, A/B tests—e.g., conversion by landing page. Why S3 + Athena: Decoupled...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in Cloud/Tools
Difficulty Split
104E|27M|48H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Adidas
Key Concepts Tested
partition

Why This Question Matters

This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like Adidas. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
75 words

Architecture: Ingestion via API Gateway + Lambda or Kinesis → S3 landing zone (JSON/Parquet) partitioned dt=YYYY-MM-DD. Glue Crawlers or manual schema → Athena tables. Query: Funnels, sessions, A/B tests—e.g., conversion by landing page. Why S3 + Athena: Decoupled storage/compute; pay per query; no cluster. Scalability: S3 unlimited; Athena concurrency unlimited. Cost: Partition by date and campaign_id; use Parquet—10x compression, column pruning. Best practice: Raw/curated/aggregated zones; lifecycle policies; query result caching; Redshift/QuickSight for dashboards if needed.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools 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.

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