What's the most cost-effective way to analyze usage patterns using utility meter analytics across millions of interval records?

The most cost-effective approach is usually to push the analysis as close to the storage layer as possible, and only move out the smallest possible amount of data for modeling or reporting.

Best-practice pattern for millions of interval records

1. Store raw interval data in a scalable, cheap format

   • Use a columnar format like Parquet or ORC
   • Partition by date / meter / region as appropriate
   • Compress data to reduce scan costs

2. Pre-aggregate before deeper analytics

   • Build hourly, daily, and monthly rollups
   • Compute basic features like:
     • peak demand
     • load factor
     • time-of-use consumption
     • anomalies
     • seasonal averages
   • This can cut data volume dramatically

3. Use distributed SQL / serverless query engines

   • Tools like BigQuery, Athena, Snowflake, Databricks SQL, or Spark
   • These are cost-effective because you pay mostly for what you scan or use

4. Apply sampling or cohorting for exploratory work

   • For pattern discovery, analyze a representative subset first
   • Then run full-scale jobs only for confirmed hypotheses

5. Separate “hot” and “cold” data

   • Keep recent interval data in fast-access tables
   • Archive older raw data and query it only when needed

6. Build a feature store or summary table for repeated analysis

   • If the same metrics are queried often, precompute them once
   • This avoids re-scanning millions of rows repeatedly

Most cost-effective architecture

A common low-cost setup is:

• Raw interval records in object storage (S3 / GCS / ADLS)
• Partitioned Parquet
• Serverless SQL for ad hoc queries
• Scheduled ETL to create aggregated feature tables
• Python/R only for the final analysis/modeling stage

If your goal is specifically “usage patterns”

Focus on metrics like:

• load shape clustering
• peak/off-peak ratios
• weekday vs weekend behavior
• seasonal usage trends
• anomaly detection
• customer segmentation by consumption profile

Rule of thumb

If you’re scanning the same raw million-row tables repeatedly, it’s usually cheaper to invest in preprocessing and aggregation than to rerun full scans every time.

If you want, I can also suggest the lowest-cost stack for this on AWS, Azure, or Google Cloud.