I will design a data archival strategy and pipeline for aurora postgresql
AWS Database Engineer, Data Pipelines and Cloud Cost Optimization
About this Gig
I help teams manage growing PostgreSQL and Aurora PostgreSQL databases by designing and building data archival pipelines that keep production tables lean without losing access to historical data.
What I deliver:
- Archival strategy: retention rules and a policy for what moves out of production
- - Pipeline build: automated jobs moving aged data from Aurora PostgreSQL to S3 in query-able format
- - Compliance-aware deletion workflows respecting retention rules
- - Validation: checks so archived data matches source before cleanup
- - Documentation: a runbook your team can run and extend
Built for teams whose Aurora PostgreSQL tables are growing fast or who need a defensible retention policy for compliance.
Note: packages below cover strategy, design and pipeline build. Full rollout across many tables needs a custom offer scoped after we talk.
Message me first to confirm engine version and data volume.
Cloud provider:
Amazon Web Services
Expertise:
Migration
Cloud computing resource:
RDS
Other Cloud Computing Services I Offer
FAQ
How long does a full data archival implementation take?
Strategy and pipeline build for one archival flow take about 1-2 weeks. Rolling this out across many tables or a full production migration is scoped as a custom offer after we talk.
Do you work with Aurora PostgreSQL only, or other engines too?
My focus is Aurora PostgreSQL and RDS PostgreSQL, but the same archival pattern works for MySQL and SQL Server. Tell me your engine and I will confirm fit.
Will archived data still be query-able, or is it locked away?
Query-able. I land archived data in S3 as Parquet and set up Athena so you can still run SQL against it, just outside your production database.
