Extract Export

Intent

Extract data from source systems and export it as stable, reusable artifacts.

When to use

• You need a repeatable data export for reporting or integrations.
• Downstream systems rely on snapshots that must be consistent.
• You need a clear boundary between source systems and consumers.

Core mechanics

• Define the extraction query or API contract.
• Normalize data to a stable schema and format.
• Version and store exports with timestamps and metadata.
• Publish data dictionaries and refresh cadence.

Implementation checklist

1. Document source systems and extraction queries.
2. Define the export schema and data dictionary.
3. Implement validation and row count checks.
4. Schedule the extract and publish cadence.
5. Store exports with retention rules and metadata.

Failure modes and mitigations

• Source schema drift -> detect and block unsafe changes.
• Partial extracts -> validate row counts and completeness.
• Stale exports -> surface freshness timestamps and alerts.

Observability and validation

• Extraction duration and error rate.
• Row count deltas between runs.
• Export freshness and last successful run time.

Artifacts

• Export schema and data dictionary.
• Sample exports and validation reports.

Related Atlas projects

• BOUSD-AeriesDataExportFormatter-Extract
• BOUSD-ClassSize-Extract
• BOUSD-DataConfDocs-Extract
• BOUSD-Enrollment-Extract
• BOUSD-MonthlyAttendance-Dashboard
• BOUSD-MonthlyAttendance-Extract
• BOUSD-PADC-Extract
• BOUSD-Staff-Technology-Dashboard
• CertManager
• PrivateGPTConf/env/default
• csv_mapper
• google-groups-guard
• gpt_kit
• informedk12-sync
• mssql_query_to_csv
• pages-aether
• privateGPT
• sql_to_csv
• titanhst_sync