Which cloud providers are supported for off-site archiving?

The system supports Amazon S3, Azure Blob Storage, and Google Cloud Storage (GCS) through integrated CLI tools.

How do I define which records get archived?

You can define archival criteria based on age thresholds (e.g., older than 1 year), status flags, or any custom SQL-based business logic.

Does this skill ensure data is not lost during the move?

Yes, the skill implements transactional handling and verification queries to confirm data integrity at the destination before any records are deleted from the source table.

Can I archive data into a different table instead of cloud storage?

Absolutely. You can choose to archive records to a separate table within the same database or a dedicated archive database.

Database Archival System

Name: Database Archival System
Author: BbgnsurfTech

byBbgnsurfTech

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Database Management

Automates the transfer of historical database records to archive tables or cold storage to optimize performance and reduce primary storage costs.

About

The Database Archival System skill provides a comprehensive framework for managing data lifecycle policies by identifying, extracting, and moving aging records from production databases to secondary storage solutions like S3, Azure Blob, or dedicated archive tables. It automates the creation of archive schemas, execution of archival scripts, and verification of data integrity, ensuring that primary databases remain lean and performant while maintaining full compliance with data retention requirements and storage efficiency standards.

Key Features

Built-in data integrity verification using checksums and row counts
Multi-cloud support for archival to AWS S3, Azure Blob, and GCS
Support for optimized storage formats including Parquet, CSV, and JSON
Automated identification of archival candidates based on age or status
3 GitHub stars
Transactional 'move' logic ensuring data is only deleted after successful archival

Use Cases

Implementing automated data retention policies for legal and regulatory compliance
Improving production query performance by offloading billions of historical records
Reducing primary database size to lower infrastructure costs and improve backup speed

About

Key Features

Built-in data integrity verification using checksums and row counts
Multi-cloud support for archival to AWS S3, Azure Blob, and GCS
Support for optimized storage formats including Parquet, CSV, and JSON
Automated identification of archival candidates based on age or status
3 GitHub stars
Transactional 'move' logic ensuring data is only deleted after successful archival

Use Cases

Implementing automated data retention policies for legal and regulatory compliance
Improving production query performance by offloading billions of historical records
Reducing primary database size to lower infrastructure costs and improve backup speed