· IoT · 2 min read
IoT data retention with storage tiers and trade offs
A pragmatic way to keep costs stable while retaining useful history.
IoT data retention is an engineering decision with direct cost and performance impact. If retention is unclear, storage grows without control and query performance suffers.
Define data tiers based on access patterns. Keep a hot tier for recent high resolution data. Move older data to a warm tier with lower resolution, and keep a cold tier for compliance or rare analytics needs. Make these tiers explicit in the platform so everyone understands the trade offs.
Compression and rollups
Roll up raw metrics into hourly or daily aggregates. Keep raw data only for the use cases that truly need it. Document what is lost during rollups so product teams know the limits of historical analysis.
Separate operational and analytics queries
Operational dashboards should not compete with heavy analytics queries. Use separate stores, partitions, or query paths to avoid contention. This keeps dashboards fast during incidents.
Introduce retention checks in your data pipeline. If a job fails, old data will accumulate quickly. Treat retention as a first class pipeline step and monitor it like any other task.
Review retention every quarter. Use storage cost reports and query logs to validate assumptions. Retention policy is not set and forget.
A pragmatic retention plan keeps costs stable without losing the history that operators and analysts need.
Design retention around query needs, not just storage costs. If the team needs to compare seasonal patterns, keep enough history in a usable form. If history is rarely used, move it out of the hot tier quickly.
Make retention visible to product teams. If a feature relies on data that will expire, it should be documented. This prevents surprise changes in dashboards and reports.
Consider compliance and regional rules. Some data must be retained for a fixed period while other data should be removed quickly. Align retention tiers with those requirements so you do not over retain or delete too early.
