Hybrid model with clear separation of concerns. WHY: OLTP and analytics have opposing requirements—low-latency writes vs. analytical scans. OLTP: Normalized schema (customers, accounts, transactions) with indexes; event sourcing for auditability. Analytics: Denormalized...
This hard-level General/Other question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, snowflake, spark) will help you answer variations of this question confidently.
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.
Hybrid model with clear separation of concerns. WHY: OLTP and analytics have opposing requirements—low-latency writes vs. analytical scans. OLTP: Normalized schema (customers, accounts, transactions) with indexes; event sourcing for auditability. Analytics: Denormalized star/snowflake—fact table (transaction_id, customer_id, product_id, amount, ts) + dimensions. Bridge via CDC. ARCHITECTURE DIAGRAM:
[OLTP DB] --CDC--> [Kafka/Debezium]
| |
v v
[Operational APIs] [Spark/Flink]
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v
[Delta Lake]
/ | \
v v v
[Bronze] [Silver] [Gold]
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v
[Snowflake/BQ]
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v
[BI / ML]
SCALABILITY: Partition by date; incremental models; single source of truth. COST: CDC vs batch—CDC reduces latency but increases infra; batch cheaper at lower freshness needs.
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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.