Smart Meter Telemetry Database Evaluation Matrix

Role: Principal Data Architect with 15+ years of experience designing high-throughput smart grid and AMI database architectures.

Context

• Utility operator: {{utility_operator}}
• Active AMI meter fleet: {{ami_device_count}}
• Ingestion interval: {{ingestion_frequency_seconds}} seconds
• Historical retention period: {{target_retention_years}} years
• Candidate database engines: {{candidate_databases}}
• Infrastructure deployment target: {{cloud_or_onprem_target}}

Task

Produce a comparative decision matrix evaluating the suitability of {{candidate_databases}} for high-density smart meter ingestion across operational dimensions at {{utility_operator}}.

Method

1. Calculate peak write throughput and IOPS requirements based on {{ami_device_count}} meters reporting every {{ingestion_frequency_seconds}} seconds.
2. Estimate total raw and compressed data volumes across {{target_retention_years}} years of operational history.
3. Benchmark query execution patterns for billing rollups and interval anomaly detection.
4. Evaluate automatic partitioning, chunking, and downsampling capabilities for each engine in {{candidate_databases}}.
5. Analyze high availability, replication lag, and node failover mechanics under {{cloud_or_onprem_target}} constraints.
6. Evaluate operational overhead, indexing costs, and enterprise support for each database candidate.
7. Score each database engine across standardized technical dimensions and synthesize findings into an evaluation matrix.

Constraints

• MUST score candidate databases using a standardized 1 to 5 scale with explicit technical justifications.
• MUST NOT recommend proprietary features that prevent open-format data extraction or portable backups.
• Focus strictly on telemetry time-series ingestion and analytical query patterns.
• Maintain architectural compliance with utility grid reliability standards.

Output format

1. Telemetry Ingestion Profile: Summary table showing calculated writes/sec, daily volume, and total storage across {{target_retention_years}} years.
2. Engine Comparison Matrix: Markdown table listing evaluation criteria as rows, candidate engines as columns, scores (1-5), and technical rationale.
3. Architectural Selection Recommendation: Summary paragraph detailing the top-ranked engine and implementation risks (max 250 words).

Self-review

• Did you calculate write throughput directly from {{ami_device_count}} and {{ingestion_frequency_seconds}}?
• Are all candidate engines named in {{candidate_databases}} evaluated equally across all rows?
• Does the matrix clearly differentiate write scalability from read query performance?