Data Architecture and Pipelines Questions
Designing data storage, integration, and processing architectures. Topics include relational and NoSQL database design, indexing and query optimization, replication and sharding strategies, data warehousing and dimensional modeling, ETL and ELT patterns, batch and streaming ingestion, processing frameworks, feature stores, archival and retention strategies, and trade offs for scale and latency in large data systems.
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Explain the role and importance of metadata and data lineage for BI teams. How does lineage help in diagnosing metric discrepancies and who should own metric definitions and transformations?
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What is a star schema and why is it commonly used for BI dashboards? Describe the components (fact and dimension tables), the concept of grain, and trade-offs compared to a highly normalized (snowflake) schema.
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Explain Change Data Capture (CDC) and why CDC is useful for incremental loading into a BI warehouse. Compare CDC with timestamp-based incremental loads and note key operational concerns a BI analyst should understand (e.g., ordering, missed events).
EasyTechnical
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Explain schema-on-read vs schema-on-write and the implications for BI teams when source schemas evolve. Include pros/cons for validation, speed, and flexibility.
MediumTechnical
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A dashboard query joining a 500M-row sales fact to several small dimensions is slow. As the BI analyst, propose practical improvements (schema, indexes, pre-aggregation, materialized views, caching) and describe how you'd measure success. Which change would you try first and why?
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