Data Retention and Deletion in a Data Platform
Data platforms tend to keep everything by default. Over time that creates storage costs, slower queries and, more importantly, risk when the platform holds personal or sensitive data longer than it should. A clear retention approach keeps the platform useful and easier to manage.
Start with the questions
- Which data does the business actually need, and for how long
- Which data is subject to legal or contractual limits
- Who can approve keeping data longer
- How will a deletion request be handled across every copy
The answers usually come from legal, security and business owners together. Data engineers turn those answers into rules the platform enforces.
Map where data lives
Raw landing zones, staging tables, modeled tables, backups, exports and test environments can all hold copies of the same records. A retention rule that only covers one layer leaves the others untouched. Lineage information helps find every place a dataset flows.
Enforce it automatically
- Set expiry on raw and staging data that is only needed briefly
- Partition large tables by date so old partitions can be dropped cleanly
- Schedule deletion jobs and log what each run removed
- Review exceptions on a regular schedule
Deletion requests
When someone asks for their data to be removed, the request has to reach every layer. Keep a list of the tables and systems that hold personal identifiers so the process is repeatable. Test the process before it is needed.
Balance with history
Analysts often want long history for trends. Aggregated or anonymized tables can keep that history while the detailed records are removed on schedule. Agree on this tradeoff early rather than after a large cleanup.
Why it matters for your career
Retention and deletion work touches infrastructure, warehousing and governance. Being able to describe how you designed and enforced a policy is useful in platform focused interviews.
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