# Building a Scalable AWS Backup System for S3 Data Warehouses

DevFeed: [Building a Scalable AWS Backup System for S3 Data Warehouses](<https://devfeed.tech/articles/building-a-scalable-data-lake-backup-system-with-aws-22559.md>)

Original publisher: [Read original article](<https://tech.scribd.com/blog/2025/building-scalable-data-warehouse-backup-system.html>)

Author: Oleh Motrunych

Published: 2025-09-22T00:00:00Z

Content type: article

Language: en

Sources: [Scribd Tech](<https://devfeed.tech/sources/scribd-tech.md>)

Topics: [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [AWS Lambda](<https://devfeed.tech/topics/aws-lambda.md>), [parquet](<https://devfeed.tech/topics/parquet.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [backup](<https://devfeed.tech/tags/backup.md>), [backups](<https://devfeed.tech/tags/backups.md>), [cleanup](<https://devfeed.tech/tags/cleanup.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [data-lake](<https://devfeed.tech/tags/data-lake.md>), [databases](<https://devfeed.tech/tags/databases.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deltalake](<https://devfeed.tech/tags/deltalake.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [incremental](<https://devfeed.tech/tags/incremental.md>), [lambda](<https://devfeed.tech/tags/lambda.md>), [lifecycle](<https://devfeed.tech/tags/lifecycle.md>), [parquet](<https://devfeed.tech/tags/parquet.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [reduce](<https://devfeed.tech/tags/reduce.md>), [retention](<https://devfeed.tech/tags/retention.md>), [s3](<https://devfeed.tech/tags/s3.md>), [terraform](<https://devfeed.tech/tags/terraform.md>)

## AI overview

This article describes a monthly backup system for S3 data warehouses that uses incremental copying, S3 Inventory validation, parallel processing, and Glacier retention. It routes smaller workloads to AWS Lambda and larger workloads to ECS Fargate, with lifecycle-based cleanup for eligible files.

## Source excerpt

We designed and implemented a scalable, cost-optimized backup system for S3 data warehouses that runs automatically on a monthly schedule. The system handles petabytes of data across multiple databases and uses a hybrid approach: AWS Lambda for small workloads and ECS Fargate for larger ones. At its core, the pipeline performs incremental backups -- copying only new or changed parquet files while always preserving delta logs -- dramatically reducing costs and runtime compared to full backups. Data is validated through S3 Inventory manifests, processed in parallel, and stored in Glacier for long-term retention. To avoid data loss and reduce storage costs, we also implemented a safe deletion workflow. Files older than 90 days, successfully backed up, and no longer present in the source are tagged for lifecycle-based cleanup instead of being deleted immediately. This approach ensures reliability, efficiency, and safety: backups scale seamlessly from small to massive datasets, compute resources are right-sized, and storage is continuously optimized.