Build vs. Buy vs. Cloud vs. On Premise Trade Offs Questions
Understanding key trade-offs in technology decision-making: (1) Build vs. Buy - custom development flexibility vs. packaged software speed/cost, (2) Cloud vs. On-Premise - operational burden, control, scalability, security, cost, (3) SaaS vs. Licensed - flexibility, upgrade frequency, customization options. Understanding implications for cost, time-to-value, flexibility, control, and ongoing support.
MediumSystem Design
0 practiced
Design a backup and disaster recovery plan for a data warehouse with an RTO of 2 hours and an RPO of 15 minutes. Compare cloud-managed options (cross-region replication, warm standby) with on-prem replication and tape/snapshot strategies. Discuss cost trade-offs and validation/testing approaches.
MediumTechnical
0 practiced
Define SLIs, SLOs, and alerting thresholds you would create for a mission-critical daily data pipeline that populates a BI warehouse. Include data freshness, success rate, end-to-end latency, schema drift detection, and data quality checks. Explain why you chose each threshold and the incident response for threshold breaches in cloud vs on-prem deployments.
MediumTechnical
0 practiced
Compute the break-even point over 3 years for Buy vs Build with these simplified inputs:Buy: License $200k/year, implementation & training $100k initial, maintenance 15%/year of license.Build: Initial development $400k, annual maintenance $80k, hardware $50k/year.Ignore discounting. Calculate cumulative costs year-by-year and state in which year buy becomes cheaper (if any). Discuss non-financial factors that influence the decision.
EasyTechnical
0 practiced
List and explain the primary trade-offs between deploying analytics and data infrastructure in the cloud versus on-premise. Address scalability, cost model (CAPEX vs OPEX), operational burden, control, network latency, data residency, and disaster recovery implications. Provide short examples tied to a data warehouse and a streaming platform.
HardTechnical
0 practiced
Design a benchmarking plan to compare Trino/Presto on-premise against cloud-native query engines (e.g., BigQuery/Snowflake) operating on comparable datasets stored in HDFS vs cloud object storage. Include dataset characteristics, workload mix, concurrency testing, warm vs cold cache, metrics to collect, and how to ensure a fair comparison.
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