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
Compare transformation approaches for BI metrics: in-warehouse SQL (dbt), batch Spark jobs, and streaming transforms (Flink/Spark Streaming). For each approach, list strengths, weaknesses, and typical use cases BI teams should choose them for.
HardTechnical
0 practiced
Propose a comprehensive testing strategy for data pipelines and BI metrics. Include unit tests, schema tests, data-diff/end-to-end tests, freshness tests, and observable metrics. Explain how to incorporate these into CI/CD and how to handle test failures that block deployments.
HardTechnical
0 practiced
You have a costly join between two 1B-row tables in your warehouse used by a critical dashboard. Discuss optimization strategies: physical design changes (partitioning, clustering), pre-aggregation, bloom filters, broadcast/hash join hints, approximation, and trade-offs around data freshness and accuracy.
HardTechnical
0 practiced
Design a real-time anomaly detection pipeline for metric feeds used in BI dashboards. Requirements: detect anomalies within 5 minutes, store anomaly events for investigation, allow drill-down to raw events, and minimize false positives. Outline components, algorithms, feature engineering, and how you would integrate alerts into BI workflows.
EasyTechnical
0 practiced
Explain Slowly Changing Dimensions (SCD) and compare Type 1 and Type 2 implementations for customer dimension changes. From a BI perspective, when would you choose Type 2 over Type 1 and how does Type 2 affect query complexity for historical reports?
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