Data Retention and TTL Policies

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Data retention and TTL policies define how long records, tables, events, and logs remain available before automatic expiration or deletion.

## Core Explanation

Agents that analyze incidents, RAG freshness, audits, or evaluation traces need to know whether data still exists. A missing event may be caused by retention policy rather than ingestion failure.

Retention policy is also a compliance boundary. Agents should not extend TTLs, disable expiration, or copy expired data into new stores without understanding legal, privacy, and operational requirements.

## Source-Mapped Facts

- DynamoDB TTL documentation says DynamoDB automatically deletes expired items within a few days of their expiration time. ([source](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html))
- MongoDB TTL index documentation says TTL indexes can automatically remove documents from a collection after a specified amount of time. ([source](https://www.mongodb.com/docs/manual/core/index-ttl/))
- BigQuery table management documentation includes table expiration settings for controlling how long a table is retained. ([source](https://cloud.google.com/bigquery/docs/managing-tables#update-table-expiration))

## Further Reading

- [DynamoDB Time to Live](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html)
- [MongoDB TTL Indexes](https://www.mongodb.com/docs/manual/core/index-ttl/)
- [BigQuery Managing Tables](https://cloud.google.com/bigquery/docs/managing-tables#update-table-expiration)