Key Interview Questions on Data Warehousing, Data Platforms, and Related Technologies
This article compiles a comprehensive set of 32 interview questions covering data warehouse fundamentals, data platform construction, modeling approaches, real‑time architectures, data quality, governance, Hive optimization, and related analytical techniques to help candidates prepare for data engineering roles.
1. What is a data warehouse? How to build a data warehouse? (If answered well, many later questions become unnecessary)
2. How to build a data middle platform? Briefly describe your understanding and approach.
3. Understanding of data warehouse, data middle platform, and data lake.
4. Traditional data warehouse components (modeling tools, ETL tools, BI reporting tools, scheduling systems).
5. Differences and similarities between traditional data warehouses and big data warehouses; major changes.
6. Most impressive project? Why? Highlights and advantages?
7. What is the most important aspect of a data warehouse?
8. Experience with real‑time data warehouses? Architecture used? Pros and cons of lambda architecture.
9. Views on kappa architecture? What about iota architecture?
10. Responsibility, communication skills, teamwork, data thinking?
11. User profiling (static/dynamic tags, statistical/rule/predictive tags, decay factor, tag weight).
12. Recommendation systems (collaborative filtering, user‑based, item‑based, SVD, distance algorithms, etc.).
13. Understanding of basic data warehouse concepts.
14. How to determine subject areas in a data warehouse? CDM?
15. How is a data warehouse layered? Purpose of each layer? Why this layering?
16. Modeling philosophies in data warehouses (dimensional modeling, normalized modeling, Data Vault); advantages, disadvantages, and selection criteria.
17. Common SCD handling methods? Pros/cons? Differences between SCD2 and chain tables.
18. Understanding of metadata? Metadata management systems?
19. How to control data quality?
20. How to perform data governance? Data asset management?
21. Hive optimization? SQL tuning, parameter tuning.
22. Data skew issues.
23. Small file problems.
24. Differences among ORDER BY, SORT BY, DISTRIBUTE BY, CLUSTER BY.
25. UDF, UDTF? Problems they solve.
26. Shuffle optimization.
27. How to rewrite ROW_NUMBER in MySQL.
28. Users who logged in for consecutive N days.
29. User retention, activity, dormant users, and re‑engaged users.
30. ANALYTIC functions such as LAG/LEAD OVER, NTILE, etc.
31. ROLLUP, CUBE, GROUPING SETS, GROUPING_ID.
32. PARTITION and bucketing; ORDER BY vs SORT BY.
This list is intended for interview preparation; answers are not provided here. For further reading, see the original article: https://www.jianshu.com/p/6ac75e9a60fe .
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