Smart Meter Time-Series Database Comparison

Role: Principal Data Architect specializing in utility-scale IoT telemetry and time-series database design.

Context

• Utility Provider: {{utility_name}}
• Active AMI Smart Meter Endpoints: {{meter_count}}
• Telemetry Sampling and Ingestion Cadence: {{ingestion_frequency}}
• Historical Data Retention Requirement: {{retention_period_years}}
• Target Cloud Infrastructure: {{cloud_platform}}
• Dominant Read and Write Workload: {{query_workload_type}}

Task

Evaluate candidate time-series database architectures against the smart meter telemetry profile and produce a structured comparative evaluation matrix to guide database engine selection.

Method

1. Calculate aggregate write ingestion volume per second based on {{meter_count}} and {{ingestion_frequency}}.
2. Estimate uncompressed and compressed storage footprints over {{retention_period_years}} on {{cloud_platform}}.
3. Assess write amplification and background compaction overhead under continuous ingestion streams.
4. Compare candidate database engines against {{query_workload_type}} analytical query latency.
5. Evaluate built-in continuous aggregate, downsampling, and automated data lifecycle policies.
6. Score integration ease with {{utility_name}} ingestion pipelines and downstream billing systems.
7. Map licensing models, managed service availability, and total cost of ownership across platforms.

Constraints

• MUST evaluate at least three distinct database engines (e.g., relational time-series, distributed columnar, wide-column).
• MUST score all evaluation dimensions on a clear 1 to 5 numerical scale with technical rationale.
• MUST NOT recommend proprietary architectures that lack multi-availability-zone failover capabilities.
• Storage and compression calculations MUST explicitly reflect typical meter payload schemas.

Output format

1. Executive Evaluation Summary (2-3 sentences outlining key findings).
2. Time-Series Database Evaluation Matrix (Markdown table with columns: Engine, Ingestion Throughput Score, Query Latency Score, Compression Efficiency, Operational Complexity, Cloud Fit, Overall Score).
3. Recommended Target Architecture (bulleted recommendation under 150 words).

Self-review

• Ingestion calculations align precisely with {{meter_count}} and {{ingestion_frequency}}.
• Matrix scores reflect the specific demands of {{query_workload_type}}.
• Storage lifecycle recommendations satisfy {{retention_period_years}} retention mandates.