Data restore tool

Domain-aware tool translating backup metadata into a concrete restoration plan for databases, files, and cloud data.

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About This Tool

Purpose of this tool is to convert abstract backup metadata into a concrete restore plan that can be executed by IT teams. It models sources such as databases, file systems, and object storage, and targets, ensuring compatibility and policy compliance. The tool uses domain logic to map a chosen restore_point to a sequence of validated steps, with a transparent estimate of duration and risk. It supports required inputs such as backup_source, restore_point, target_system, and data_scope, and offers advanced options for multi-backup orchestration and dry runs. The output is a structured runbook plus quantified estimates to support change control and post-restore verification. Benefits include reduced downtime, auditable restore processes, and improved data integrity during DR exercises. Core features include restore point validation, source-target compatibility checks, data_type aware restoration, and exportable results for governance. Use cases cover database restores, file system recovery, and cloud object restore, across on premise and cloud environments.

How to Use

Provide inputs: backup_source, restore_point, target_system, data_scope.
Choose optional recovery settings if needed.
Run calculation to generate a restore plan with steps and estimates.
Review the plan, including duration and risk score.
Execute the recommended steps in a controlled schedule and verify data integrity.

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Frequently Asked Questions

Find Quick Answers

What data sources are supported?
The tool supports common backup formats and metadata from databases, file systems, object storage, and snapshot services; it relies on cataloged backups with timestamps and version info to enable accurate planning, validation checks, and auditable runbooks.
How is duration estimated?
Duration is computed from a baseline restore time plus a throughput adjustment that accounts for data size, data_type complexity, and network factors; external conditions are not measured during planning, so estimates may be refined during actual execution.
What if inputs are missing or inconsistent?
Missing inputs trigger explicit validation errors and block planning until corrected; inconsistent restore_point or incompatible backup_source may require alternative sources or a different point in time, with guidance to revalidate inputs and re-run the planning step.

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