Academic Research Data Storage Engine Comparison

Role: Principal Research Data Architect specializing in distributed scientific data management.

Context

• Research Organization: {{research_institution}}
• Primary Data Format: {{primary_data_modality}}
• Ingestion Throughput Target: {{ingestion_throughput_target}}
• Governance Standard: {{metadata_governance_standard}}
• Compliance Boundary: {{compliance_boundary}}
• Budget Tier: {{budget_tier}}

Task

Generate a structured decision matrix comparing at least three database storage engines to identify the optimal platform for scientific data sharing and long-term research archival.

Method

1. Review the ingest requirements defined by {{ingestion_throughput_target}} against standard database write patterns.
2. Analyze how {{primary_data_modality}} structures dictate schema flexibility, indexing strategies, and relational versus non-relational storage needs.
3. Identify three distinct database engine architectures appropriate for {{research_institution}} and its resource tier {{budget_tier}}.
4. Map each candidate engine against compliance requirements enforced by {{compliance_boundary}}.
5. Evaluate support for metadata cataloging and FAIR data principles aligned with {{metadata_governance_standard}}.
6. Score each candidate across query performance, schema evolution, access controls, and maintenance overhead.
7. Synthesize findings into a final comparative matrix accompanied by an architectural recommendation summary.

Constraints

• MUST evaluate at least three distinct database engine paradigms (e.g., distributed SQL, document/graph, columnar object storage).
• MUST explicitly score each engine against {{compliance_boundary}} and {{metadata_governance_standard}}.
• Do not include vendor sales assertions without explicit technical justification.
• Scoring MUST use a consistent 1-5 scale with defined scoring criteria.

Output format

1. Executive Context (2-3 sentences summarizing the deployment target).
2. Candidate Engine Overview (bulleted list defining the 3 engines evaluated).
3. Comparative Evaluation Matrix (Markdown table with columns: Candidate Engine, Schema Fit, {{compliance_boundary}} Compliance, Ingestion Capacity, Governance Fit, Total Score [out of 25]).
4. Architectural Recommendation (1 paragraph justifying the top-scoring engine).

Self-review

• Verify all 6 context variables are directly addressed in the evaluation criteria.
• Confirm table columns strictly match the specified layout.
• Check that scoring totals mathematically sum up correctly.