In IoT programs I have led, storage costs creep up unless we set retention rules early and stick to them. The surprise usually arrives a few months in, when nobody remembers who decided to keep raw data forever. 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.
Example retention policy: hot, warm, cold for device telemetry
A fleet sends 1 billion events per month. The team keeps 30 days of raw events in a hot store for troubleshooting. After 30 days, events are aggregated into hourly metrics and moved to warm storage for 12 months. After a year, only daily aggregates remain in a cold store for trend analysis. This reduces storage cost while preserving the data needed for product decisions. The retention policy also includes a delete schedule for raw data that is no longer needed for support or compliance. What trips teams up: The painful part is that they look harmless at first.
- Keeping raw data forever because it is easier than defining a policy.
- Aggregating too early and losing the ability to debug incidents.
- Ignoring access patterns, which makes cold data too slow to query.
- Missing legal requirements for data deletion or regional storage.
- Not monitoring storage growth, which hides cost regressions.
Retention checklist
- Define how long raw, aggregated, and summary data should live.
- Decide the aggregation windows and the queries they must support.
- Automate tiering and deletions with tested jobs.
- Tag data by tenant and region to support compliance rules.
- Track storage costs monthly and alert on anomalies.
- Review the policy when product or regulatory needs change.
Query patterns and access tiers: Define the queries that the business and support teams actually run. If most queries look at the last seven days, keep that data in a hot tier with fast indexes. If analytics jobs only need monthly aggregates, move them to a slower tier. The goal is to match storage cost to how often the data is accessed.
Consider adding a cache layer for the most common aggregates. This can make dashboards fast without keeping raw data in the expensive tier. Compliance and privacy considerations: Retention policies must follow privacy requirements. Some data may need to be deleted after a fixed period, while other data must be retained for regulatory reasons. Tag data by tenant and jurisdiction and enforce deletion jobs that are audited. This keeps data governance from becoming a manual process.
Cost estimation example
Estimate monthly costs per tier. For example, hot storage might cost 25 USD per TB per month, warm storage 10 USD per TB, and cold storage 2 USD per TB. If you keep 10 TB hot, 50 TB warm, and 200 TB cold, the monthly cost is predictable and easy to explain. Use these estimates to validate whether a new feature needs a longer hot window or more aggregation.
A simple cost view also helps product teams understand the impact of retention choices. Data access SLAs: Define expected query times for each tier. Hot data might need sub second responses, warm data might allow seconds, and cold data might allow minutes. Setting these expectations prevents teams from over investing in fast storage for rarely accessed data.
