Data Modeling and Architecture Questions

Design and modeling principles for transactional and analytical data systems. Topics include entity relationship modeling, normalization and denormalization trade offs, dimensional modeling with fact and dimension tables and star and snowflake schemata, indexing strategies, partitioning and sharding, and schema design for performance and maintainability. Cover data pipelines and integration patterns including extract transform load and extract load transform approaches, data warehousing and data lake concepts, ETL orchestration, and how sources feed into reporting and business intelligence systems. Also include considerations for data quality, governance, and the differences between online transaction processing and online analytical processing workloads.

HardTechnical

0 practiced

As a BI analyst leading governance, propose a set of concrete policies and processes to ensure data quality, access control, metadata management, and data contracts. Explain how these policies are operationalized in ETL jobs, dashboards, and how ownership and SLAs are enforced.

MediumTechnical

0 practiced

Compare ETL and ELT patterns and recommend which is more suitable for a cloud data warehouse like Snowflake or BigQuery when ingesting large semi-structured logs. Discuss compute cost, transformation locality, and maintainability trade-offs.

EasyTechnical

0 practiced

What are Slowly Changing Dimensions (SCD)? Describe SCD Type 1, Type 2, and Type 3 with short examples of when each is appropriate for business reporting and the historical implications of each choice.

HardSystem Design

0 practiced

Design an analytics architecture to support near-real-time dashboards that ingest up to 100,000 events per second and must push updates with end-to-end latency under 5 seconds. Describe choices for ingestion, streaming processing, storage/OLAP engine, schema design for low-latency reads, and how BI tools will connect.

MediumTechnical

0 practiced

Explain indexing strategies for read-heavy analytical workloads. Compare b-tree, bitmap, and columnar indexes (or columnstore structures), and describe scenarios where each provides the most benefit for aggregation and filter-heavy BI queries.

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