Utility meter readings stream in as write-heavy time-series appends that grow sparse columns over long periods. Which NoSQL family is typically suited to this pattern?

Utility meter readings are write-heavy time-series appends that accumulate sparse attributes over long horizons—exactly the pattern wide-column (column-family) stores were popularized to handle at scale. They spread writes across partitions and tolerate sparse columns without dense relational padding. A single-row spreadsheet emailed nightly cannot absorb municipal meter cardinality or provide distributed durability. Graph databases focused on social friend edges optimize traversals, not high-rate numeric time-series appends. An in-memory queue without a durable column-oriented layout may buffer briefly but is not the durable analytical or operational store for years of meter history. Exam caveat: wide-column suitability assumes a partition/clustering key design around meter id and time; a poor key still creates hot spots. Operational check: model meter_id + timestamp as the access key, ingest a burst of readings in staging, and confirm write throughput scales across nodes while sparse optional attributes do not force full-row rewrites. In production terms for wide-column meter time-series ingest, assign an owning team, wire a dashboard that shows the pattern is healthy under peak civic load, and require a staging drill that rehearses failure and recovery before resident impact. Revisit the choice when traffic shape, retention, or integrity needs change, and keep a named rollback owner on the change record. Re-test both the happy path and the failure path for wide-column meter time-series ingest after material dependency upgrades so regressions are caught before the next public release.