Business Intelligence and Analytics Performance Questions
Performance considerations for business intelligence and analytics tools and pipelines. Topics include extract versus live connections, incremental refresh strategies, aggregated tables and precomputation, dashboard profiling, minimizing visual complexity, and caching strategies for reporting layers. Candidates should understand when to denormalize data for reporting, how to monitor query times inside BI tools, and trade offs between real time versus pre aggregated reporting.
MediumTechnical
77 practiced
Design SLOs and instrument metrics to monitor BI platform health: query latency percentiles (p50/p90/p99), cache hit ratios, refresh success/failure rates, query concurrency, and data freshness. Explain threshold choices, alerting strategies, and what dashboards you would provide for on-call engineers.
EasyTechnical
90 practiced
Compare basic caching approaches for reports: BI-tool in-memory caching, query-result caching at the data warehouse, and CDN-level caching for public reports. For each, describe typical TTL strategies and scenarios where it is appropriate or problematic (e.g., highly personalized dashboards).
EasyTechnical
65 practiced
When and why would you denormalize transactional data to create reporting tables? Discuss benefits such as reduced join complexity and faster queries, and downsides like increased storage, update complexity, and potential stale data. Give two concrete examples where denormalization improved a dashboard's performance.
HardTechnical
81 practiced
Given predictable query patterns and large dataset growth, design cost-optimization strategies that include selective pre-aggregation, materialized views, data compaction/tiering, columnar compression, and query routing to cold storage. Explain trade-offs, where cost savings come from, and how to measure ROI.
MediumTechnical
135 practiced
A dashboard performs expensive joins between a 100M-row fact table and 10-20 small dimension tables causing slow queries. Propose modeling changes (denormalized flattened tables, pre-join materialized views), clustering/index strategies, and estimate expected performance gains and trade-offs such as update complexity and storage footprint.
Unlock Full Question Bank
Get access to hundreds of Business Intelligence and Analytics Performance interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.