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.
MediumTechnical
0 practiced
Explain in detail how to implement SCD Type 2 for a customer dimension. Provide an example schema (columns) and SQL pseudocode for inserting new rows and marking old rows as expired when a customer's address changes.
MediumTechnical
0 practiced
List the key elements of data governance a BI team should implement (access controls, PII masking, lineage, approvals). For each element, describe a practical implementation approach in a cloud data platform and trade-offs between strict controls and analyst productivity.
EasyTechnical
0 practiced
Describe basic index concepts that matter to BI dashboards: clustered vs non-clustered indexes, selectivity, and how indexes can accelerate or slow down analytical queries. When might adding an index hurt more than help?
MediumTechnical
0 practiced
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?
EasyTechnical
0 practiced
Describe ETL versus ELT approaches for feeding a BI/analytics warehouse. For each approach, name common tools/technologies, explain where transforms occur, and give examples of when a BI team should prefer ETL over ELT (and vice versa).
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